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Diversity plays a major role when a student is looking for a tutor to better understand some concepts or an entire course. In fact, algorithms for recommending potential tutors have to take into account several aspects of diversity that may be critical to successful tutoring. The tutor must have the appropriate competencies in the subject matter. In addition, he/she must be able to convey the knowledge and skills associated with the tutoring topic. Moreover, the personality traits of the tutee and the tutor can facilitate or hinder the learning process during tutoring. This study presents the experience of ‘SOS TUTORÍA UC’, a responsive web application aimed at facilitating academic assistance among students. Special emphasis is placed on the importance of incorporating dimensions of diversity that can inform the artificial intelligence algorithms of the potential tutor recommendation system. Indeed, competence in the topic tutored is the first diversity criterion for recommending more knowledgeable potential tutors. In addition, the tutee has to choose whether to look for tutors with personalities that are “different”, “similar” or “indifferent” to his or her own preferences for academic assistance on the specific topic. To achieve this, ‘SOS TUTORÍA UC’ is integrated with the WeNet platform, which provides user management services and user recommendation algorithms. The results of the testing of the recommendation system were positive with regard to the criterion of competence, while the criterion of personality should be addressed for improvement. In order to improve the tutor-tutee matching process, participants emphasized the importance of considering the criterion of personality traits in addition to competence. They also requested additional information and parameters to facilitate tutor selection. [ABSTRACT FROM AUTHOR]

To enhance the precision of the music recommendation environment system, a novel design approach has been introduced, utilizing multi-label propagation and hierarchical clustering analysis for a dual music recommendation environment. First of all, the process model of music recommendation environmental system is built based on music recognition system, which is composed of music signal preprocessing module, music model, sound model and music recognizer; second, on the basis of further study on the clustering validity, a new clustering validity function is established by describing the intra-class compactness and inter-class separation of clustering through fuzzy similarity relation; finally, the validity of the proposed music double recommendation environmental system using multi-label propagation hierarchical clustering analysis is verified by simulation experiment. The results show that the recommendation method based on comprehensive evaluation of user characteristics is suitable for single-category users, while the recommendation method based on multi-category evaluation is suitable for multi-category users. This approach offers an effective and precise means to enhance the accuracy and customization of music recommendation systems, thereby increasing user satisfaction. [ABSTRACT FROM AUTHOR]

Recommendation systems (RS) play a crucial role in assisting individuals in making suitable selections from an extensive array of products or services. This significantly mitigates the predicament of being overwhelmed by excessive information. RS finds powerful utility in online industries by vending products over the internet or furnishing online services. Given the potential for business expansion through their implementation, RS is relevant in such domains. This comprehensive review article overviews RS and its diverse variations and extensions. Specifically, this review provides a thorough comparative analysis for each method that encompasses many techniques employed in RS, encompassing content-based filtering, collaborative filtering, hybrid, and miscellaneous approaches. Notably, the article delves into the manifold applications of RS across various practical domains. Additionally, the assortment of evaluation metrics utilized across RS is explored. Finally, we conclude by encapsulating the distinct challenges RS encounters, which enhance their precision and dependability. [ABSTRACT FROM AUTHOR]

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures. [ABSTRACT FROM AUTHOR]

Recommendation systems are an effective solution to deal with information overload, particularly in the e-commerce sector, in which sequential recommendation is extensively utilized. Sequential recommendations aim to acquire users' interests and provide accurate recommendations by analyzing users' historical interaction sequences. To improve recommendation performance, it is vital to take into account the long- and short-term interests of users. Despite significant advancements in this domain, some issues need to be addressed. Conventional sequential recommendation models typically express each item with a uniform embedding, ignoring evolutionary patterns among item attributes, such as category, brand, and price. Moreover, these models often model users' long- and short-term interests independently, failing to adequately address the issues of interest drift and short-term interest evolution. This study proposes a new model, the Feature-aware Long-Short Interest Evolution Network (FLSIE), to address the above-mentioned issues. Specifically, the model uses explicit feature embedding to represent item attribute information and employs a two-dimensional (2D) attention mechanism to distinguish the significance of individual features in a specific item and the relevance of each item in the interaction sequence. Furthermore, to avoid the issue of interest drift, the model employs a long-term interest guidance mechanism to enhance the representation of short-term interest and adopts a gated recurrent unit with attentional update gate to model the dynamic evolution of users' short-term interest. Experimental results indicate that our presented model outperforms existing methods on three real-world datasets. [ABSTRACT FROM AUTHOR]

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model's efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it's crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach. [ABSTRACT FROM AUTHOR]

Application-specific reasoning mechanisms (ASRMs) development is a rapidly growing domain of systems engineering. A demonstrative implementation of an active recommender system (ARS) was realized to support designing ASRMs and to circumvent procedural obstacles by providing context-sensitive recommendations. The specific problem for the research presented in this paper was the development of a synthetic validation agent (SVA) to simulate the decisional behaviour of designers and to generate data about the usefulness of the recommendations. The fact of the matter is that the need for the SVA was raised by the pandemic, which prevented involving groups of human designers in the recommendation testing process. The reported research had three practical goals: (i) development of the logical fundamentals for the SVA, (ii) computational implementation of the SVA, and (iii) application of the SVA in data generation for the evaluation of usefulness of recommendation. The SVA is based on a probabilistic decisional model that quantifies decisional options according to the assumed decisional tendencies. The three key concepts underlying the SVA are (i) decisional logic, (ii) decisional knowledge, and (iii) decisional probability. These together enable generation of reliable data about the decisional behaviours of human designers concerning the obtained recommendations. The completed tests proved the above assumption. [ABSTRACT FROM AUTHOR]

There is a growing need for recommender systems and other ML-based systems as an abundance of data is now available across all industries. Various industries are currently using recommender systems in slightly different ways. These programs utilize algorithms to propose appropriate products to consumers based on their prior choices and interactions. Moreover, Systems for recommending events to users suggest pertinent happenings that they might find interesting. As opposed to an object recommender that suggests books or movies; event-based recommender systems typically require distinct algorithms. A developed event recommendation method is introduced which includes two stages: feature extraction and recommendation. In stage, I, a Set of features like personal willingness, community willingness, informative content, edge weight, and node interest degree are extracted. Stage II of the event recommendation system performs a hybrid classification by combining LSTM and CNN. In the LSTM classifier, optimal tuning is done by Improvised Cat and Mouse optimization (ICMO) algorithm. The results of the ICMO technique at an 80% training percentage have the maximum sensitivity value of 95.19%, whereas those of the existing approaches SSA, DINGO, BOA, and CMBO have values of 93.89%, 93.35%, 92.36%, and 92.24%. Finally, the best result is then determined by evaluating the whole performance. [ABSTRACT FROM AUTHOR]

A cross-domain recommendation system is an intelligent recommendation technology that integrates multiple fields or types of data. It can cross independent information islands, effectively integrate and complement data resources, and improve recommendation performance. This paper proposes a transfer contrast learning method based on model-level data enhancement for cross-domain recommendations. This method first obtains the initial embeddings of the two domains using item-based collaborative filtering, after which it enhances the transformer network with model-level data through contrastive learning to pre-train the source domain data. The pre-trained transformer network parameters are then transferred and fine-tuned before being applied to tasks on the target domain data. The information link from the source domain to the target domain is effectively constructed, and it has been proven to improve the accuracy and effectiveness of the target domain on real datasets. [ABSTRACT FROM AUTHOR]

This study delves into the intricacies of booking behaviors, focusing on the preference for tropical coastal cities during colder seasons, given the burgeoning need for personalized and intelligent travel recommendations in the modern tourism landscape. Traditional booking systems, with their static algorithms, have often neglected the dynamic interplay of environmental factors and user preferences, necessitating an evolution towards more sophisticated, adaptive models. To address this, the research employed advanced machine learning and artificial intelligence techniques, amalgamating data from diverse sources like major tourism websites, hotel platforms, and social media. The methodology involved meticulous data preprocessing to ensure data quality and depth, followed by the implementation of deep learning models and time series analysis, which were instrumental in deciphering complex patterns and offering predictive insights. This comprehensive approach facilitated the integration of various environmental factors such as weather conditions and temporal events, enabling the system to craft hyper-personalized recommendations that resonate with user preferences and environmental nuances. The findings were illuminating, unveiling a significant inverse relationship between temperature and bookings and highlighting the influential role of temporal events in driving bookings. The advanced predictive models showcased commendable accuracy, underscoring the transformative potential of such intelligent systems in enhancing user experiences and engagement in the tourism sector. These insights pave the way for the development of innovative strategies that can significantly elevate user satisfaction and industry growth. The study concludes that the integration of machine learning and AI in booking systems can revolutionize the tourism industry by offering unprecedented levels of personalization and adaptability, catering to the diverse and evolving needs of modern travelers. [ABSTRACT FROM AUTHOR]

Conversational recommender systems use natural language conversations to elicit user preferences and recommend items proactively. Existing methods based on graph neural networks have been proven to be effective in exploiting knowledge graphs. However, node positions are often treated as constants, which leads to the neglect of graph connectivity due to fuzzy processing. In addition, although the transformer has significant advantages in understanding the text, its secondary computational complexity may be incapable when dealing with long texts. In order to solve these problems, we propose an additive positional conversational recommender model called APCR. This model converts the pair product of transformer into a linear operation, and uses the Laplacian eigenvector to build a location graph. The extended graph neural network captures the topology structure of the location knowledge graph. Specifically, we design an encoder based on additive attention to break through the bottleneck of long text. Furthermore, we develop a recommendation model based on a positional graph neural network to match items with dialogue context, thereby capturing the graph topology. Extensive experiments on the REDIAL dataset show significant improvements in our proposed model over the state-of-the-art methods in recommendation and dialogue generation evaluations. [ABSTRACT FROM AUTHOR]

Recently, most studies in the field have focused on integrating reviews behind ratings to improve recommendation performance. However, two main problems remain (1) Most works use a unified data form and the same processing method to address the user and the item reviews, regardless of their essential differences. (2) Most works only adopt simple concatenation operation when constructing user-item interaction, thus ignoring the multilevel relationship between the user and the item, which may lead to suboptimal recommendation performance. In this paper, we propose a novel Asymmetric Multi-Level Interactive Attention Network (AMLIAN) integrating reviews for item recommendation. AMLIAN can predict precise ratings to help the user make better and faster decisions. Specifically, to address the essential difference between the user and the item reviews, AMLIAN uses the asymmetric network to construct user and item features using different data forms (document-level and review-level). To learn more personalized user-item interaction, the user ID and item ID and some processed features of user reviews and item reviews are respectively used for multilevel relationships. Experiments on five real-world datasets show that AMLIAN significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Link prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, social theory, topology, and similarity-based. The main issue is that path-based methods ignore topological features, while structure-based methods also fail to combine the path and structured-based features. As a result, a new technique based on the shortest path and topological features' has been developed. The method uses both local and global similarity indices to measure the similarity. Extensive experiments on real-world datasets from a variety of domains are utilized to empirically test and compare the proposed framework to many state-of-the-art prediction techniques. Over 100 iterations, the collected data showed that the proposed method improved on the other methods in terms of accuracy. SI and AA, among the existing state-of-the-art algorithms, fared best with an AUC value of 82%, while the proposed method has an AUC value of 84%. [ABSTRACT FROM AUTHOR]

In existing sequential recommendation systems, user behavior data are directly used as training data for the model to complete the training process and address recommendation tasks. However, user-generated behavioral data inevitably contains noise, and the use of the Transformer's recommendation model may lead to overfitting on such noisy data. To address this issue, we introduce a sequence recommendation algorithm model named FAT-Rec, which incorporates fusion filters and converters through joint training. By employing joint training methods, we establish both a transformer prediction layer and a CTR prediction layer. Toward the end of the model, we assign weights and sum up the losses from the Transformer and CTR prediction layers to derive the final loss function. Experimental results on two widely used datasets, MovieLens and Goodbooks, demonstrate a significant enhancement in the performance of the proposed FAT-Rec recommendation algorithm compared with seven comparative models. This validates the efficacy of the fusion filter and transformer within the context of sequence recommendation tasks under the joint training mechanism. [ABSTRACT FROM AUTHOR]

A recommendation system serves as a distributed information filter, predicting customer preferences in reviews, ratings, and comments. Analysing customer behaviour aids in understanding needs and predicting intentions. E-commerce tracks product usage and sentiment to provide a personalized network based on consumer preference modelling. The challenge lies in optimizing item selection for suitable consumers to enhance performance. To address this, an imperative is the item recommendation approach for modelling future consumer behaviour. However, traditional machine learning methods often overlook dynamic product recommendations due to evolving user interests and changes in preferences reflected in customer ratings, causing cold-start issues. To overcome these challenges, a comprehensive deep learning approach is introduced. This approach incorporates a deep neural network for consumer preference prediction, utilizing a multi-task learning paradigm to accommodate variations in consumer ratings. The research contribution lies in applying this network to predict consumer preference scores based on latent multimodal information and item characteristics. Initially, the architecture manages changing consumer aspects and preferences by extracting features and latent factors from customer review rating data. These latent factors include customer demographic information and other concealed features that signify preferences based on experiences and behaviours. Extracted latent features are processed using a sentiment analysis model to generate embedding latent features. A finely-tuned deep neural network with hyper-parameter adjustments serves as a prediction network, forming a customer performance-oriented recommendation system. It processes embedded latent features along with associated sentiments to achieve high prediction accuracy, reliability, and latency. The deep learning architecture, enriched with consumer-specific discriminative information, generates an objective function for item recommendations with minimal error, significantly enhancing predictive performance. Empirical experiments on Amazon review datasets validate the proposed model's performance, showcasing its enhanced effectiveness and scalability in handling substantial data volumes. [ABSTRACT FROM AUTHOR]

User feedback data (e.g., clicks, dwell time in the product detail page) have been incorporated in the training process of many ranking models for better performance. Such approaches are widely used in many ranking applications, including search and recommendation. Recently, the inherent biases in user feedback data have been studied, which indicates how the users' behaviors can be affected by factors other than relevancy. By identifying and removing these biases, the ranking models can be further improved. Researchers have developed a variety of debiasing methods on different bias factors. Most of them only focus on one type of bias and pay little attention to different types of bias from a unified perspective. In this paper, we conduct a comprehensive study of bias focusing on the application of ranking problems in recommender systems which is highly important for the research of web intelligence. Then, we share our experiences derived from designing and optimizing unbiased models to improve feeds recommendation. To uncover the effects of biases and achieve better ranking performance, we propose several unbiased models and compare with state-of-the-art models. We conduct extensive offline experiments on real datasets and validate the effectiveness of our method by performing online A/B testing in a real-world recommender system. [ABSTRACT FROM AUTHOR]

Conversational recommender systems provide users with item recommendations via interactive dialogues. Existing methods using graph neural networks have been proven to be an adequate representation of the learning framework for knowledge graphs. However, the knowledge graph involved in the dialogue context is vast and noisy, especially the noise graph nodes, which restrict the primary node's aggregation to neighbor nodes. In addition, although the recurrent neural network can encode the local structure of word sequences in a dialogue context, it may still be challenging to remember long-term dependencies. To tackle these problems, we propose a sparse multi-hop conversational recommender model named SMCR, which accurately identifies important edges through matching items, thus reducing the computational complexity of sparse graphs. Specifically, we design a multi-hop attention network to encode dialogue context, which can quickly encode the long dialogue sequences to capture the long-term dependencies. Furthermore, we utilize a variational auto-encoder to learn topic information for capturing syntactic dependencies. Extensive experiments on the travel dialogue dataset show significant improvements in our proposed model over the state-of-the-art methods in evaluating recommendation and dialogue generation. [ABSTRACT FROM AUTHOR]

News consumption has shifted over time from traditional media to online platforms, which use recommendation algorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their preferences and reading behavior. In comparison to domains such as movies or e-commerce, where recommender systems have proved highly successful, the characteristics of the news domain (e.g., high frequency of articles appearing and becoming outdated, greater dynamics of user interest, less explicit relations between articles, and lack of explicit user feedback) pose additional challenges for the recommendation models. While some of these can be overcome by conventional recommendation techniques, injecting external knowledge into news recommender systems has been proposed in order to enhance recommendations by capturing information and patterns not contained in the text and metadata of articles, and hence, tackle shortcomings of traditional models. This survey provides a comprehensive review of knowledge-aware news recommender systems. We propose a taxonomy that divides the models into three categories: neural methods, non-neural entity-centric methods, and non-neural path-based methods. Moreover, the underlying recommendation algorithms, as well as their evaluations are analyzed. Lastly, open issues in the domain of knowledge-aware news recommendations are identified and potential research directions are proposed. [ABSTRACT FROM AUTHOR]

This paper took the course recommendation as an example to prove that the personalized intelligent detection and recommendation algorithm system provides a convenient means for this problem. In view of the problems such as low recommendation accuracy, lack of personalization and long running time of the current personalized course recommendation system, this paper studied the personalized intelligent detection and recommendation algorithm system with the help of big data technology, hoping to solve the existing problems. The general search results of the traditional recommendation system have been unable to meet the needs of users. In order to improve the course recommendation system, this paper used big data technology to build a scientific framework, and made an in-depth analysis of various problems existing in the traditional system and strove to solve them. The results showed that using big data intelligent systems to search for ten sets of intelligent courses had a search accuracy of between 96% and 98%, indicating that big data can achieve more accurate and efficient results in the construction of intelligent course recommendation systems, and can construct a new course recommendation system to meet user needs. [ABSTRACT FROM AUTHOR]

In view of the technical defects of the existing grey relational projection method, a new grey compromise relational bidirectional projection method is proposed. By incorporating the information expression advantage of picture hesitant fuzzy number, the distance formula of picture hesitant fuzzy statistics is constructed based on the centralized trend measurement and discrete trend measurement in descriptive statistics. On this basis, a multi-attribute recommendation method of picture hesitant fuzzy grey compromise relational bidirectional projection is proposed by combining compromise idea and bidirectional projection technology. The validity and advantage of this method are verified by numerical analysis, which also suggested the rationality of the picture hesitant fuzzy statistical distance and the grey compromise relational bidirectional projection method. [ABSTRACT FROM AUTHOR]

The considerable fluctuation of the stock market caused by COVID-19 tends to have a tremendous and long-lasting adverse impact on the economy. In this work, we propose a novel methodology to investigate this impact on the Chinese medical stock market. We examine changes in the stock network structure using the Triangulated Maximally Filtered Graph (TMFG), which is computationally faster and more adaptable to enormous datasets. Additionally, we develop the LoGo model, which combines a local-global approach in its construction, to predict the stock prices of the Chinese medical stock market. In addition to traditional predictors, we incorporate daily new infected numbers as an additional predictor to reflect the impact of COVID-19. We select data from the 2019-2020 period and divide it into two datasets: one for the period during COVID-19 and another for the period before COVID-19. Firstly, we compute the grey correlation coefficients between stocks instead of standard correlation coefficients. We use these coefficients to build the TMFG, enabling us to identify which stocks played the leading roles. Subsequently, we choose six stocks to build the price prediction models. Compared with the LSTM and SVR models, the LoGo models demonstrates higher accuracy, achieving an average accuracy of 71.67 percent. Furthermore, the execution time of the Logo models is 200 times faster than that of the SVR models and 50 times faster than that of the LSTM models. [ABSTRACT FROM AUTHOR]

In an age of big data and information overload, recommendation systems have evolved rapidly. Throughout the traditional design of interior spaces, the specialised nature of the work and the high rate of human involvement has led to high costs. With the continuous development of artificial intelligence technology, it provides a favourable environment for reducing the development cost of the system. This study proposes a two-stage modelling scheme based on deep learning networks for the intelligent design of display space layouts, divided into two parts: matching and layout, which greatly improves design efficiency. The research results show that through comparison tests, its prediction accuracy reaches more than 80%, which can well meet the matching requirements of household products. The training number of Epochs is between 15 and 30, its training curve tends to saturate and the best accuracy can reach 100%, while the running time of the hybrid algorithm proposed in this study is only 20.716 s, which is significantly better compared with other algorithms. The proposed hybrid algorithm has a running time of only 20.716 s, which is significantly better than other algorithms. The approach innovatively combines deep learning technology with computer-aided design (CAD), enabling designers to automatically generate display space layouts with good visibility and usability based on complex design constraints. This study presents an innovative application of the research methodology by combining quantitative and qualitative methods to analyse the data. The application of both methods provides a more comprehensive understanding of the problem under study and provides insight into the key factors that influence the results. The findings of this study can provide useful insights for policy makers and practitioners. [ABSTRACT FROM AUTHOR]

The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network (TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets. [ABSTRACT FROM AUTHOR]

With the explosive increase of information, recommendation system is applied in a variety of areas. However, the performance of recommendation system is limited due to issues such as data sparsity, cold starts and poor semantic understanding. In order to make full use of external information to assist recommendation, deeply mine the semantic information of review text and further improve the performance of recommendation system, a deep recommendation system based on knowledge graph and review text (Drs-kgrt) is proposed in this paper. In Drs-kgrt, knowledge graph, review text and the social records between users are used as auxiliary information to improve recommendation performance. Firstly, the review text is divided into user review text and item review text. BERT (Bidirectional Encoder Representation from Transformers) is used to accurately understand semantic information in user review text and the social records between users. The trust relationship between users and user preferences are fully mined to form user feature vectors. Secondly, BERT and knowledge graph entity recognition link technology are combined to extract item attribute feature entities and their associated entities. The fine-grained features of the items are analyzed to form item feature vectors. Thirdly, based on the scoring matrix, latent vectors of users and items are obtained by matrix decomposition. The deep features of users and items are generated based on user feature vectors, item feature vectors, latent vectors of users and items, the deep recommendation system is established to predict user scores for items. Finally, experiments are conducted on the Douban dataset and Amazon Movie Review dataset, the results show that the proposed algorithm can achieve better performance compared with other benchmark recommendation algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, with the application of 5G, graph-based recommendation algorithms have become a research hotspot. Graph neural networks encode the graph structure information in the node representation through an iterative neighbor aggregation method, which can effectively alleviate the problem of data sparsity. In addition, more and more information graph can be used in collaborative filtering recommendation, such as user social information graph, user or item attributed information graph, etc. In this paper, we propose a novel heterogeneous information fusion based graph collaborative filtering method, which models graph data from different heterogeneous graph, and combines them together to enhance presentation learning. Through information propagation and aggregation, our model can learn the latent embeddings effectively and enhance the performance of recommendation. Experimental results on different datasets validate the outperformance of the proposed framework. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. In the network structure of DPRN, as the network depth increases, the number of convolutional kernels also increases linearly or nonlinearly. On the one hand, in the DPRN block, the size of the receptive field is only 3 × 3, which results in insufficient network ability to extract feature map information of different filter sizes. On the other hand, the number of convolution kernels in the second 1x1 convolution will be multiplied by a coefficient relative to the first convolution, which can cause overfitting to some extent. In order to overcome these weaknesses, we introduce the inception-like structure on the basis of the DPRN network which is called by pyramid inceptional residual networks (PIRN). In addition, we also discuss the performance of PIRN network with squeeze and excitation (SE) mechanism and regularization term. Furthermore, some results in network performance are discussed when adding a stochastic depth networkto the PIRN model. Compared to DPRN, PIRN achieved better results on the CIFAR10, CIFAR100, and Mini-ImageNet datasets. In the case of using zero-padding, the multiplicative PIRN with SE mechanism achieves the best result of 95.01% on the CIFAR10 dataset. Meanwhile, on the CIFAR100 and Mini-ImageNet datasets, the additive PIRN network with a network depth of 92 achieves the best results of 76.06% and 65.86%, respectively. According to the experimental results, our method has achieved better accuray than that of DPRN with same network settings which demonstrate its effectiveness in generalization ability. [ABSTRACT FROM AUTHOR]

In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user's or patient's illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user's extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine. [ABSTRACT FROM AUTHOR]

Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and predicts ratings more precisely by utilizing the first two phases' outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model. [ABSTRACT FROM AUTHOR]

The multi-layer feature pyramid structure, represented by FPN, is widely used in object detection. However, due to the aliasing effect brought by up-sampling, the current feature pyramid structure still has defects, such as loss of high-level feature information and weakening of low-level small object features. In this paper, we propose FI-FPN to solve these problems, which is mainly composed of a multi-receptive field fusion (MRF) module, contextual information filtering (CIF) module, and efficient semantic information fusion (ESF) module. Particularly, MRF stacks dilated convolutional layers and max-pooling layers to obtain receptive fields of different scales, reducing the information loss of high-level features; CIF introduces a channel attention mechanism, and the channel attention weights are reassigned; ESF introduces channel concatenation instead of element-wise operation for bottom-up feature fusion and alleviating aliasing effects, facilitating efficient information flow. Experiments show that under the ResNet50 backbone, our method improves the performance of Faster RCNN and RetinaNet by 3.5 and 4.6 mAP, respectively. Our method has competitive performance compared to other advanced methods. [ABSTRACT FROM AUTHOR]

Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users' individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users' satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user's interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user's preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms. [ABSTRACT FROM AUTHOR]

A large percentage of the worldwide population is affected by chronic diseases, leading to a burden of the patient and the national healthcare systems. Recommendation systems are used for the personalization of healthcare due to their capacity of performing predictive analyses based on the patient's clinical data. This systematic literature review presents four research questions to provide an overall state of the art of the use of recommendation systems applied to the healthcare of patients with chronic diseases. Disease management was identified as the main purpose of the systems proposed in the literature. However, few solutions provide support to physicians in the clinical decision-making. Ontologies and rule-based systems were the artificial intelligence techniques most used in the systems since they can easily implement clinical guidelines. Current challenges of these systems include the low adherence, data sparsity, heterogeneous data, and explainability, that affect the success of the recommendation system. The results also show that there are few systems that provide support to patients with multiple chronic conditions. The findings of this literature review should be considered in the development of future recommendation systems that aim to support the management of chronic diseases. [ABSTRACT FROM AUTHOR]

The advent of the information age has made accurate search for information a challenge. In this paper, we analyze intelligent recommendations for innovative entrepreneurial projects based on collaborative filtering algorithms. Collaborative filtering is one of the most widely used and successful techniques in recommendation systems. In this paper, an interest migration function plus time is introduced to address the shortcomings of traditional collaborative filtering recommendation algorithms. Meanwhile, this paper builds an intelligent recommendation engine system for innovative entrepreneurial projects based on the Hadoop open-source distributed computing framework, sustainable PSCM, and Mahout collaborative filtering recommendation engine technology. This paper uses experiments to test and evaluate the overall performance of the distributed recommendation platform and the improved collaborative filtering recommendation algorithm. It is found that the algorithm outperforms similar algorithms in terms of data volume and coverage of recommended innovation and entrepreneurship projects. This is sufficient to show that the collaborative filtering algorithm and sustainable PSCM are useful for the intelligent recommendation analysis of innovative entrepreneurial projects. [ABSTRACT FROM AUTHOR]

Online shopping is a form of electronic commerce that enables customers to purchase products directly from the seller over the internet using a web browser or a mobile application. With the growing number of research papers on Deep Learning (DL)-based recommendations, DL models have proven to be effective in Recommender systems. Hence in this paper, relevant product accessories recommendation is proposed using deep learning and histogram features. The proposed recommendation describes the novel way of content-based filtering which divides the work into two phases. In phase 1, the implemented approach determines the color and category of the product. In phase 2, the recommendation system retrieves the matching accessories for the chosen product. For phase 1, Decision Tree, Linear Regression, Logistic Regression, Naïve Bayes, Long Short-Term Memory (LSTM), and a novel method of Stack dense Recurrent Neural Network (RNN) along with the color histogram is applied to identify the category and color of the product. It is justified that the proposed technique of stack dense RNN achieves 98% accuracy. In phase 2, from the Machine Learning (ML) perspective, Random Forest (RF) is employed for the recommendation. The RF model finds the matching accessories for the chosen product and retrieves the top relevant items. [ABSTRACT FROM AUTHOR]

Multimedia item characteristics are used in domains, such as recommender systems and information retrieval. In this work we distinguish two main groups of item characteristics: (i) item-centric item characteristic (ICIC) and (ii) user-centric item characteristic (UCIC). With the term ICIC we denote a characteristic of an item that (a) has roots in the item and (b) has the same value for all users, for example, the duration of a song. With the term UCIC, we denote a characteristic of an item that (a) has roots in the perception of the user from an item characteristic and (b) exhibits some variance across different users, for example, the perceived emotion of a song. We survey recent work that covers various types of UCIC, acquisition methods of UCIC, and domain usage of UCIC. We identify gaps in the research and provide guidelines for future work. [ABSTRACT FROM AUTHOR]

Web service recommender systems have a fundamental role in the selection, composition and substitution of services. Indeed, they are used in several application areas such as Web APIs and Cloud Computing. Likewise, Deep Learning techniques have brought undeniable advantages and solutions to the challenges faced by recommendations in all areas. Unfortunately, the field of Web services has not yet benefited well from these deep methods, moreover, the works using these methods for Web services domain are very recent compared to the works of other fields. Thus, the objective of this paper is to study and analyze state-of-the-art work on Web services recommender systems based on Deep Learning techniques. This analysis will help readers wishing to work in this field, and allows us to direct our future work concerning the Web services recommendation by exploiting the advantages of Deep Learning techniques. [ABSTRACT FROM AUTHOR]

Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset. [ABSTRACT FROM AUTHOR]

A recommendation System (RS) is an emerging technology to figure out the user's interests and intentions. As the amount of data increases exponentially, it is hard to analyze the user intentions and trigger the recommendation accordingly. In this research work, a novel recommendation system called the Deep Knowledge Graph based Attribute Preserving Recommendation (DKG-APR) is presented to analyze massive data and provide personalized recommendations to users. The Deep Knowledge Graph for Recommendation System (DKG-RS) uses Deep Convolutional Neural Network (DCNN) and attention mechanism to explicitly model high-order connections in knowledge graphs. According to empirical findings, Knowledge Graph Attention Network (KGAT) performs better than other state-of-the-art recommendation techniques like RippleNet and Neural FM. Additional research demonstrates the effectiveness of embedding propagation for high-order relation modeling and the advantages of the attention mechanism for interpretability.The results also show that user information is crucial in the recommendation system, as seen from the optimal node-drop-out ratio of 0.2, which led to the best recall value of 0.2 for all datasets. [ABSTRACT FROM AUTHOR]

Point-of-interest (POI) recommendation has become one of the research highlights in the field of recommender systems due to the prosperity of location-based social networks in recent years. Various techniques have been proposed to improve the performance of the personalized recommendation service. Embedding-based methods have shown promising effect and attracted great attention for their flexibility and efficiency. Bayesian Personalized Ranking (BPR), as a famous optimization algorithm, has been widely used to learning the parameters of Embedding-based models in the recommendation scenario. However, existing Bayesian Personalized Ranking and its follow-up methods ignore the unique user preference when constructing the positive and negative samples, leading a suboptimal performance. To overcome this limitation, we propose a novel method named preference-aware Bayesian Personalized Ranking (PABPR) according to empirical analyses on real-world datasets. The empirical analyses show that a user tend to visit a POI with categories which have been visited before. Thus, the key idea of PABPR is to introduce such user behaviors into the sample constructing process. PABPR is a general method which could be used for training various Embedding methods. Extensive experiments show that PABPR can lead a superior model performance compare to BPR and its variant methods. [ABSTRACT FROM AUTHOR]

Recent technological advancements have enabled users to conduct more sophisticated business transactions via Wi-Fi enabled networks. Typically, a compromised access point (CAP) can handle all traffic between a user and an Internet server, thus becoming a serious security hazard. In addition, an attacker can easily control the entire network using the CAP remotely and compromise as many victims as possible to form a botnet. This paper presents a hybrid recommendation prediction model for forecasting CAP attacks based on network traffic in a private network. This model combines various prediction techniques likethe time-series model, the kNN model and cross association algorithm for attack prediction. This hybrid blacklisting recommendation system effectively improves the prediction rate significantly as well as the robustness against poisoning attacks. [ABSTRACT FROM AUTHOR]

The e-learning is necessary in this fast internet world, especially during this pandemic situation, to continue education without any interruption and it is used reduce the educational cost significantly when reduces the energy loss. Generally, machine learning and deep learning algorithms are used to identify patterns that facilitate learning and help learners understand concepts easily. Many content recommendation systems are available for assisting learners as e-learning applications by providing the required study materials. Despite the fact that existing recommendation systems struggle to provide precise content to e-learners due to the availability of a massive volume of data on the internet and other repositories. For this purpose, we propose a new content recommendation system for recommending suitable content to learners according to their interests and learning capabilities. The proposed content recommendation system employs a newly proposed semantic-aware hybrid feature optimizer that incorporates new optimization algorithms such as the Enhanced Personalized Best Cuckoo Search Algorithm (EpBestCSA) and the Enhanced Harris Hawks Optimization Algorithm (EHHOA) for selecting suitable features that aid in improving prediction accuracy, as well as a newly proposed Deep Semantic Structure Model (DSSM) that incorporates Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). According to the experimental results, the proposed model outperforms other recommendation systems in terms of precision, recall, f-measure, and prediction accuracy. The ten-fold cross validation is done to test the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users' specific preferences. A user-item bipartite graph could be used to understand the differences in motivation. However, existing research has not clearly proposed and modeled the factors that affect the differences, ignoring the similarities between user pairs and item pairs, preventing them from capturing fine-grained user preferences more effectively. In addition to the two points mentioned above, most GNN-based models for recommendation have the following two limitations: First, the model's accuracy depends on the number of observed interactions in the dataset. Secondly, node representations are vulnerable to noisy interactions. This work has developed a novel recommendation model called "Multi-Attribute and Implicit Relationship Factors With Self-Supervised Learning for Collaborative filtering" (MIS-CF), which explicitly proposes and models multi-attribute and implicit relationship factors for collaborative filtering recommendation. Meanwhile, an auxiliary self-supervised learning task is designed to help the downstream task optimize the node representation. MIS-CF aggregates multi-attribute spaces through the user-item bipartite graph and establishes user-user and item-item graphs to model the similar relationship information of neighbor pairs through a memory model. The self-supervised learning task generates contrastive learning via self-discrimination, thus mining the rich auxiliary signals within the data, improving the accuracy and robustness of our model. Moreover, the sparse regularizer is utilized to alleviate the overfitting problem. Extensive experimental results on three public datasets not only show the significant performance and robustness gain of the proposed model but also prove the effectiveness and interpretability of fine-grained implicit factors modeling. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have been successfully used to learn user and item representations for Collaborative Filtering (CF) based recommendations (GNN-CF). Besides the main recommendation task in a GNN-CF model, contrastive learning is taken as an auxiliary task to learn better representations. Both the main task and the auxiliary task face the noise problem and the distilling hard negative problem. However, existing GNN-CF models only focus on one of them and ignore the other. Aiming to solve the two problems in a unified framework, we propose a Multi-Mixing strategy for GNN-based CF (M2GCF). In the main task, M2GCF perturbs embeddings of users, items and negative items with sample-noise by a mixing strategy. In the auxiliary task, M2GCF utilizes a contrastive learning mechanism with a two-step mixing strategy to construct hard negatives. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed model. Further experimental analysis shows that M2GCF is robust against interaction noise and is accurate for long-tail item recommendations. [ABSTRACT FROM AUTHOR]

Conventional recommendation techniques utilize various methods to compute the similarity among products and customers in order to identify the customer preferences. However, such conventional similarity computation techniques may produce incomplete information influenced by similarity measures in customers' preferences, which leads to poor accuracy on recommendation. Hence, this paper introduced the novel and effective recommendation technique, namely Deep Embedded Clustering with matrix factorization (DEC with matrix factorization) for the collaborative recommendation. This approach creates the agglomerative matrix for the recommendation using the review data. The customer series matrix, customer series binary matrix, product series matrix, and product series binary matrix make up the agglomerative matrix. The product grouping is carried out to group the similar products using DEC for retrieving the optimal product. Moreover, the bi-level matching generates the best group customer sequence in which the relevant customers are retrieved using tversky index and angular distance. Also, the final product suggestion is made using matrix factorization, with the goal of recommending to clients the product with the highest rating. Also, according to the experimental results, the developed DEC with the matrix factorization approach produced better results with respect to f-measure values of 0.902, precision values of 0.896, and recall values of 0.908, respectively. [ABSTRACT FROM AUTHOR]

In recent times, recommendation systems provide suggestions for users by means of songs, products, movies, books, etc. based on a database. Usually, the movie recommendation system predicts the movies liked by the user based on attributes present in the database. The movie recommendation system is one of the widespread, useful and efficient applications for individuals in watching movies with minimal decision time. Several attempts are made by the researchers in resolving these problems like purchasing books, watching movies, etc. through developing a recommendation system. The majority of recommendation systems fail in addressing data sparsity, cold start issues, and malicious attacks. To overcome the above-stated problems, a new movie recommendation system is developed in this manuscript. Initially, the input data is acquired from Movielens 1M, Movielens 100K, Yahoo Y-10-10, and Yahoo Y-20-20 databases. Next, the data are rescaled using a min-max normalization technique that helps in handling the outlier efficiently. At last, the denoised data are fed to the improved DenseNet model for a relevant movie recommendation, where the developed model includes a weighting factor and class-balanced loss function for better handling of overfitting risk. Then, the experimental result indicates that the improved DenseNet model almost reduced by 5 to 10% of error values, and improved by around 2% of f-measure, precision, and recall values related to the conventional models on the Movielens 1M, Movielens 100K, Yahoo Y-10-10, and Yahoo Y-20-20 databases. [ABSTRACT FROM AUTHOR]

Nowadays, people share their opinions through social media. This information may be informative or non-informative. Filtering informative information from social media plays a challenging issue. Nevertheless, people will interact more with that particular disaster event on social media, primarily when a disaster occurs. They share their opinion through some textual information such as tweets or posts. In this work, we propose a generalized approach for categorizing the informative and non-informative media on Twitter. We collected the seven natural disaster events from the crisisNLP. These datasets are different disaster events containing people's opinions on that specific event. We pre-process the information, which converts the tweet information into machine-understandable vectors. Various machine learning algorithms have processed these vectors. We consider the individual performance of each ML algorithm on different disaster datasets upon choosing the best five algorithms for voting techniques. We tested the performance with matrices such as accuracy, precision, recall, and F1-score. We compared our results with existing models in which our proposed model performed better than other existing state of the art models. [ABSTRACT FROM AUTHOR]

The Graphic Interchange Format (GIF) is a bitmap picture format that has a series of perpetually repeating images or silent movies that may be viewed without the user having to click and start them. GIFs are frequently used to visually represent emotions that are expressed through body language such as gestures, movements, and facial expressions. Computing may be used to recognise thoughts and other emotions like desire, interest, sentiments, etc. by using emotional expressions or movements as face markers or properties in GIFs. The ability to predict emotions in GIFs may make it easier to express oneself on social media and convey a person's attitude or personality. Emotion detection in GIFs may be utilised for a range of purposes, e.g., developing a recommendation system, detecting inappropriate content, sentiment identification from GIF-induced sentiment as perceived by person and creating a GIF tag generating system. This study discusses the prior contributions made towards emotion identification in GIFs and describes a method for detecting seven different emotion classes (Happy, Anger, Sad, Surprise, Disgust, Fear, and Neutral) in GIFs by combining an activity recognition network with face emotional expression. The suggested deep neural network, RNN, LSTM approach produced an F1-score of 0.89 and an accuracy of 88 percent. [ABSTRACT FROM AUTHOR]

The internet and social networks produce an increasing amount of data. There is a serious necessity for a recommendation system because exploring through the huge collection is time-consuming and difficult. In this study, a multi-modal classifier is introduced which makes use of the output from dual deep neural networks: GRU for text analysis and Faster R-CNN for image analysis. These two networks reduce overall complexity with minimal computational time while retaining accuracy. More precisely, the GRU network is utilized to process movie reviews and the Faster RCNN is used to recognize each frames of the movie trailers. Gated Recurrent Unit (GRU) is a well-known variety of RNN that computes sequential data across recurrent structures. Faster RCNN is an enhanced version of Fast RCNN, it combines with the rectangular region proposals and with the features is extract by the ResNet-101. Initially, the trailer of the movie is manually splitted into frames and these frames are pre-processed using fuzzy elliptical filter for image analysis and the movie reviews are also tokenized for text analysis. The pre-processed text is taken as an input for GRU to classify offensive and non-offensive movies and the pre-processed images are taken as an input for Faster R-CNN to classify violence and non- violence movies based on the extracted features from the movie trailer. Afterwards, the four classified outputs are given as input for fuzzy decision-making unit for recommending best movies based on the Mamdani fuzzy inference system with gauss membership functions. The performance of the dual deep neural networks was evaluated using the specific parameters like specificity, precision, recall, accuracy and F1 score measures. The proposed GRU yields accuracy range of 97.73% for reviews and FRCNN yields the accuracy range of 98.42% for movie trailer. [ABSTRACT FROM AUTHOR]

Nowadays, the model compression method of knowledge distillation has drawn great attentions in Recommender systems (RS). The strategy of bidirectional distillation performs the bidirectional learning for both the teacher and the student models such that these two models can collaboratively improve with each other. However, this strategy cannot effectively exploit representation capabilities of each item and lack of the interpretability for the importance of items. Thus, how to develop an effective sampling scheme is still valuable for us to further study and explore. In this paper, we propose an improved rank discrepancy-aware item sampling strategy to enhance the performance of bidirectional distillation learning. Specifically, by employing the distillation loss, we train the teacher and student models to reflect the fact that a user has partiality for the unobserved items. Then, we propose the improved rank discrepancy-aware sampling strategy based on feedback learning mechanism to transfer just the useful information which can effectively enhance each other. The key part of the multiple distillation training aims to select valuable items which can be re-distilled in the network for training. The proposed technique can effectively solve the problem of high ambiguity in nature for recommender system. Experimental results on several real-world recommender system datasets well demonstrate that the improved bidirectional distillation strategy shows better performance. [ABSTRACT FROM AUTHOR]

Motivated by Hema Freshs new-retail case, we noticed that an effective recommender system is a common way to attract the consumers' purchasing behaviors and thus enlarge the profit of platform as well as retailers. With the aim of increasing the benefits of all parties in the platform, this paper focusing on not only increasing the effectiveness of the recommender platform but also the evaluation system of measuring the interests of consumer, retailers and platform. In this paper, the interests of the third-party platform are added into the evaluation system, the profit of the third-party platform as an evaluation index is taken and a 0–1 integer programming model is established which sets the profit of the platform as the objective function. The result of the proposed model and algorithm indicate that: (1) The relevance of products has a significant impact on platform recommendation when the consumers are selecting products. When the correlations of the products are high, the algorithms of selecting the products will have a lower capacity of 1% compared with the algorithm without products correlations. (2) The evaluation of the target products from the target consumers is quite different from the heterogeneity assumptions. When the consumer presentation is taken into consideration, it is hard to evaluate the consumer presence because of the strictly requirement of data for the platform recommendation system. (3) The proposed two-stage solution for the platform recommendation system is optimized in time and space complexity. Total optimization of the proposed method is 30% higher than the greedy algorithms. The two stages are combined together to obtain the approximate solution, and finally provide a reasonable and feasible recommendation for the third-party platform. [ABSTRACT FROM AUTHOR]

Counterfeit drugs are without a doubt becoming a greater hazard to consumers and the pharmaceutical sector. As a result, real-time visibility of drug manufacturing and management is required. The proposed system uses Ethereum blockchain as the main technology. The primary advantage of blockchain technology is that the transactions are maintained in immutable digital ledger format and it may be read easily without jeopardizing the users' security and privacy. In our proposed system, the admin validates and adds the manufacturers. The manufacturer after registering and logging in can perform tasks like adding the drug and seller list. The seller can place order to the manufacturer which the manufacturer can accept or reject. The seller can update status of order of accepted orders to delivered. The customer can view the order details by entering the serial number on the drug package. Any transaction or exchange that occurs in the network is recorded in the chain. It functions similarly to other networks, but blockchain technology is distinguished by the fact that no data can be removed or altered by anyone in the network. No changes to the network can be made unless it has been validated by all of the network's authorized users. All the information stored can be read by anybody so to incorporate more security, AES has been used to store data in the blockchain. The use of AES encryption technique distinguishes this system from all the existing implementations. Thus, this makes it easy to trace to the exact point in the supply chain and detect any counterfeit drugs in movement. As an extension to the drug counterfeit prevention system a Drug Recommendation System is also performed using the ensemble model with a combination of Random Forest and Logistic Regression for sentiment analysis training. Furthermore, when compared to the existing Linear SVM model, which has an accuracy of 90.39%, the suggested model has the best accuracy of 93.31%. Using the obtained sentiment for each drug, the drug is predicted accurately for the specified medical condition. [ABSTRACT FROM AUTHOR]