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Cloud computing has become ubiquitous in modern society, facilitating various applications ranging from essential services to online entertainment. To ensure that quality of service (QoS) standards are met, cloud frameworks must be capable of adapting to the changing demands of users, reflecting the societal trend of collaboration and dependence on automated processing systems. This research introduces an innovative approach for link prediction and user cloud recommendation, leveraging nano-grid applications and deep learning techniques within a cloud computing framework. Heuristic graph convolutional networks predict data transmission links in cloud networks. The trust-based hybrid decision matrix algorithm is then employed to schedule links based on user recommendations. The proposed model and several baselines are evaluated using real-world networks and synthetic data sets. The experimental analysis includes QoS, mean average precision, root mean square error, precision, normalized square error, and sensitivity metrics. The proposed technique achieves QoS of 73%, mean average precision of 59%, root mean square error of 73%, precision of 76%, normalized square error of 86%, and sensitivity of 93%. The findings suggest that integrating nano-grid and deep learning techniques can effectively enhance the QoS of cloud computing frameworks. [ABSTRACT FROM AUTHOR]

With the rise of online shopping due to the COVID-19 pandemic, Recommender Systems have become increasingly important in providing personalized product recommendations. Recommender Systems face the challenge of efficiently extracting relevant items from vast data. Numerous methods using deep learning approaches have been developed to classify fashion images. However, those models are based on a single model that may or may not be reliable. We proposed a deep ensemble classifier that takes the probabilities obtained from five pre-trained models such as MobileNet, DenseNet, Xception, and the two varieties of VGG. The probabilities obtained from the five pre-trained models are then passed as inputs to a deep ensemble classifier for the prediction of the given item. Several similarity measures have been studied in this work and the cosine similarity metric is used to recommend the products for a classified product given by a deep ensemble classifier. The proposed method is trained and validated using benchmark datasets such as Fashion product images dataset and Shoe dataset, demonstrating superior accuracy compared to existing models. The results highlight the potential of leveraging transfer learning and deep ensemble techniques to enhance fashion recommendation systems. The proposed model achieves 96% accuracy compared to the existing models. [ABSTRACT FROM AUTHOR]

Recommender systems (RS) primarily rely on user feedback as a core foundation for making recommendations. Traditional recommenders predominantly rely on historical data, which often presents challenges due to data scarcity issues. Despite containing a substantial wealth of valuable and comprehensive knowledge, user reviews remain largely overlooked by many existing recommender systems. Within these reviews, there lies an opportunity to extract valuable insights, including user preferences and contextual information, which could be seamlessly integrated into recommender systems to significantly enhance the accuracy of the recommendations they provide. This paper introduces an innovative approach to building context-aware RS, spanning from data extraction to ratings prediction. Our approach revolves around three essential components. The first component involves corpus creation, leveraging Dbpedia as a data source. The second component encompasses a tailored named entity recognition (NER) mechanism for the extraction of contextual data. This NER system harnesses the power of advanced models such as bidirectional encoder representations from transformers (BERT), bidirectional long short term memory (Bi-LSTM), and bidirectional conditional random field (Bi-CRF). The final component introduces a novel variation of factorization machines for the prediction of ratings called contextual factorization machines. Our experimental results showcase robust performance in both the contextual data extraction phase and the ratings prediction phase, surpassing the capabilities of existing state-of-the-art methods. These findings underscore the significant potential of our approach to elevate the quality of recommendations within the realm of contextaware recommender systems. [ABSTRACT FROM AUTHOR]

Background: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics. Aims: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis. Methods: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual. Results: Our cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems. Conclusion: The result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations. [ABSTRACT FROM AUTHOR]

Click‐through rate (CTR) prediction is a pivotal challenge in recommendation systems. Existing models are prone to disturbances from noise and redundant features, hindering their ability to fully capture implicit and higher‐order feature interactions present in sparse feature data. Moreover, conventional dual‐tower models overlook the significance of layer‐level feature interactions. To address these limitations, this article introduces Gate‐enhanced Multi‐space Interactive Neural Networks (GMINN), a novel model for CTR prediction. GMINN adopts a dual‐tower architecture in which a multi‐space interaction layer is introduced after each layer in the dual‐tower deep neural network. This layer allocates features into multiple subspaces and employs matrix multiplication to establish layer‐level interactions between the dual towers. Simultaneously, a field‐aware gate mechanism is proposed to extract crucial latent information from the original features. Experimental validation on publicly available datasets, Criteo and Avazu, demonstrates the superiority of the proposed GMINN model. Comparative analyses against baseline models reveal that GMINN substantially improves up to 4.09% in AUC and a maximum reduction of 7.21% in Logloss. Additionally, ablation experiments provide further validation of the effectiveness of GMINN. [ABSTRACT FROM AUTHOR]

The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient's clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines. [ABSTRACT FROM AUTHOR]

With the massive amount of data available on the Internet, many users face problems when they need access to the information and goods they need. Adapting information to user needs has become a complex and time-consuming process. Recommender systems are powerful tools for guiding users in electronic settings to information, services, and goods of interest. With the ability to identify users and predict their preferences, recommender systems can extract information likely to be of interest to users from vast amounts of data and save time and energy by providing them with relevant recommendations. Therefore, in this paper, a framework for a recommender system is proposed, which consists of four main phases. In the first phase, the preprocessing operation is performed. The purpose of this phase is to create an ontology model for products based on the convolutional neural network method. Data collection operations are carried out in the second phase. The purpose of this phase is to receive and store user information based on behavior and different criteria. The third phase is related to creating an ontology. The purpose of this phase is to create an ontology model for user behavior using OWL libraries. Finally, in the last phase, finding similarities and presented a proposal. The purpose of this phase is the necessary calculations of the proposed framework in the similarity finding section using ontology models. Simulation results show that the proposed framework measures MAE (more than 10 and 15%), RMSE (more than 12 and 16%), precision (less than 10 and 13%), recall (more than 25 and 28%), F1-score (higher than 25 and 30%) and Score of User (higher than 15 and 17%) outperform two related approaches, namely CF and CF+ Ontology, respectively. [ABSTRACT FROM AUTHOR]

Knowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been benefiting from the adoption of KGs is that of Recommender Systems (RSs). Feeding a RS with data coming from a KG improves recommendation accuracy, diversity, and novelty, and paves the way to the creation of interpretable models that can be used for explanations. This possibility of combining a KG with a RS raises the question whether such an addition can be performed in a plug-and-play fashion – also with respect to the recommendation domain – or whether each combination needs a careful evaluation. To investigate such a question, we consider all possible combinations of (i) three recommendation tasks (books, music, movies); (ii) three recommendation models fed with data from a KG (and in particular, a semantics-aware deep learning model, that we discuss in detail), compared with three baseline models without KG addition; (iii) two main encyclopedic KGs freely available on the Web: DBpedia and Wikidata. Supported by an extensive experimental evaluation, we show the final results in terms of accuracy and diversity of the various combinations, highlighting that the injection of knowledge does not always pay off. Moreover, we show how the choice of the KG, and the form of data in it, affect the results, depending on the recommendation domain and the learning model. [ABSTRACT FROM AUTHOR]

Recommendation systems are crucial in navigating the vast digital market. However, user data's dynamic and non-stationary nature often hinders their efficacy. Traditional models struggle to adapt to the evolving preferences and behaviours inherent in user interaction data, posing a significant challenge for accurate prediction and personalisation. Addressing this, we propose a novel theoretical framework, the non-stationary transformer, designed to effectively capture and leverage the temporal dynamics within data. This approach enhances the traditional transformer architecture by introducing mechanisms accounting for non-stationary elements, offering a robust and adaptable solution for multi-tasking recommendation systems. Our experimental analysis, encompassing deep learning (DL) and reinforcement learning (RL) paradigms, demonstrates the framework's superiority over benchmark models. The empirical results confirm our proposed framework's efficacy, which provides significant performance enhancements, approximately 8% in LogLoss reduction and up to 2% increase in F1 score with other attention-related models. It also underscores its potential applicability across accumulative reward scenarios with pure reinforcement learning models. These findings advocate adopting non-stationary transformer models to tackle the complexities of today's recommendation tasks. [ABSTRACT FROM AUTHOR]

Abstract Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work.

Texture describes the unique features of an image. Therefore, texture classification is a crucial task in computer vision. Various CNN-based deep learning methods have been developed to classify textures. During training, the deep-learning model undergoes an end-to-end procedure of learning features from low to high levels. Most CNN architectures depend on high-level features for the final classification. Hence, other low- and mid-level information was not prioritized for the final classification. However, in the case of texture classification, it is essential to determine detailed feature information within the pattern to classify textures as they have diversity and irregularity in images within the same class. Therefore, the feature information at the low- and mid-levels can also provide meaningful information to distinguish the classes. In this study, we introduce a CNN model with a feature retention module (FRM) to utilize features from numerous levels. FRM maintains the texture information extracted at each level and extracts feature information through filters of various sizes. We used three texture datasets to evaluate the proposed model combined with the FRM. The experimental results showed that learning using different levels of features together assists in improving learning performance more than learning using high-level features. [ABSTRACT FROM AUTHOR]

Motivation The rapid expansion of Bioinformatics research has led to a proliferation of computational tools for scientific analysis pipelines. However, constructing these pipelines is a demanding task, requiring extensive domain knowledge and careful consideration. As the Bioinformatics landscape evolves, researchers, both novice and expert, may feel overwhelmed in unfamiliar fields, potentially leading to the selection of unsuitable tools during workflow development. Results In this article, we introduce the Bioinformatics Tool Recommendation system (BTR), a deep learning model designed to recommend suitable tools for a given workflow-in-progress. BTR leverages recent advances in graph neural network technology, representing the workflow as a graph to capture essential context. Natural language processing techniques enhance tool recommendations by analyzing associated tool descriptions. Experiments demonstrate that BTR outperforms the existing Galaxy tool recommendation system, showcasing its potential to streamline scientific workflow construction. Availability and implementation The Python source code is available at https://github.com/ryangreenj/bioinformatics_tool_recommendation. [ABSTRACT FROM AUTHOR]

Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work. [ABSTRACT FROM AUTHOR]

With the development of deep learning, deep neural methods have been introduced to boost the performance of Collaborative Filtering (CF) models. However, most of the models rely solely on the user-item heterogeneous graph and only implicitly capture homogenous information, which limits their performance improvement. Although some state-of-the-art methods try to utilize additional graphs to make up, they either merely aggregate the information of multiple graphs in the step of initial embedding or only merge different multi-graph information in the step of final embedding. Such one-time multi-graph integration leads to the loss of interactive and topological information in the intermediate process of propagation. This paper proposes a novel Multi-Graph iterative fusion Recommendation Framework (MGRF) for CF recommendation. The core components are dual information crossing interaction and multi-graph fusing propagation. The former enables repeated feature crossing between heterogeneous nodes throughout the whole embedding process. The latter repeatedly integrates homogeneous nodes as well as their topological relationships based on the constructed user-user and itemitem graphs. Thus, MGRF can improve the embedding quality by iteratively fusing user-item heterogeneous graph, user-user and item-item homogeneous graphs. Extensive experiments on three public benchmarks demonstrate the effectiveness of MGRF, which outperforms state-of-the-art baselines in terms of Recall and NDCG. [ABSTRACT FROM AUTHOR]

In recent years, sequential recommendation systems have become a hot topic in the field of recommendation system research. These systems predict future user actions or preferences by analyzing their historical interaction sequences, such as browsing history and purchase records, and then recommend items that users may be interested in. Among various sequential recommendation algorithms, those based on the Transformer model have become a focus of research due to their powerful self-attention mechanisms. However, one of the main challenges faced by sequential recommendation systems is the noise present in the input data, such as erroneous clicks and incidental browsing. This noise can disrupt the model's accurate allocation of attention weights, thereby affecting the accuracy and personalization of the recommendation results. To address this issue, we propose a novel method named "weight adjustment framework for self-attention sequential recommendation" (WAF-SR). WAF-SR mitigates the negative impact of noise on the accuracy of the attention layer weight distribution by improving the quality of the input data. Furthermore, WAF-SR enhances the model's understanding of user behavior by simulating the uncertainty of user preferences, allowing for a more precise distribution of attention weights during the training process. Finally, a series of experiments demonstrate the effectiveness of the WAF-SR in enhancing the performance of sequential recommendation systems. [ABSTRACT FROM AUTHOR]

Learners are confronted with an ever-growing array of diverse and complex educational resources as music education increasingly moves to online platforms. Traditional resource curation methods, which rely heavily on educators, fall short of meeting the dynamic needs of modern students. To address this issue, we present a novel recommendation system for music e-learning resources that combines the power of blockchain technology with a hybrid deep learning model. Our model combines blockchain's robust security and transparency features with advanced deep learning algorithms, enhancing the personalization and efficiency of resource recommendations. A backpropagation neural network with K nearest neighbor classification, traditional collaborative filtering (CF), and an improved CF algorithm are used in the hybrid approach. For the back propagation neural network algorithm, K nearest neighbor classification algorithm, traditional collaborative filtering (CF) and improved CF algorithm, the accuracy rate of improved CF algorithm is higher, reaching 95%. Comparing the proposed model with the association rule-based recommendation model and the content-based recommendation model, the model constructed in this study received high evaluation from experts, with an average score of 98, and more than 97% of them gave a high score of 95 or more, and the evaluation of experts tended to be consistent. Overall, the model proposed in this study can make better recommendations for music education learning resources and bring users a good learning experience, so this study has some practical application value. This research demonstrates a highly effective, blockchain-enhanced recommendation system for music e-learning resources. Our model has significant practical value and potential for adoption in online music education platforms because it provides tailored educational content and an enhanced learning experience. [ABSTRACT FROM AUTHOR]

In e-commerce, the session-based personalized recommendation remains challenging due to the limited user information within a single session. Merely relying on a user's local data is insufficient. It is vital to consider global data, extracting insights from sessions across all users to glean collaborative information. However, using all session information will waste computing resources. Moreover, much of the global data may not be pertinent to the current user, thereby undermining the quality of recommendations. To address this, we introduce the concept of personalized next session (PNS), selectively referencing sessions most relevant to the user to enhance the limited local data. This work is the first to adopt a deep network architecture study that incorporates the concept of PNS to recommend the next item for a user in the current session. We evaluated our approach on several real-world datasets, and the results show that our model outperforms state-of-the-art recommendation methods. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user–item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in deep learning have spurred the development of hybrid models, which extend matrix factorization, particularly with autoencoders, to capture nonlinear item relationships. Despite these advancements, many proposed models often neglect dynamic changes in the rating process and overlook user features. This paper introduces IUAutoTimeSVD++, a novel hybrid model that builds upon autoTimeSVD++. By incorporating item–user features into the timeSVD++ framework, the proposed model aims to address the static nature and sparsity issues inherent in existing models. Our model utilizes a contractive autoencoder (CAE) to enhance the capacity to capture a robust and stable representation of user-specific and item-specific features, accommodating temporal variations in user preferences and leveraging item characteristics. Experimental results on two public datasets demonstrate IUAutoTimeSVD++'s superiority over baseline models, affirming its effectiveness in capturing and utilizing user and item features for temporally adaptive recommendations. [ABSTRACT FROM AUTHOR]

Social media and e-commerce are the two most prominent and quickly expanding industries today. These two areas exhibit the greatest influence on platform users. Numerous new people sign up for these networks on a daily basis. This platform offers extremely quick user networking and communication. These platforms are used to create an online product-based recommender system that will help grow online business by recommending products. Online product recommendations are entirely dependent on the views, feedback, and comments of consumers. Online recommender systems have become a regular part of consumers' everyday routines, with their widespread use observed in e-commerce, social networking platforms, and news websites. This paper offers a novel framework for product recommendation based on sentiment analysis (SA) and collaborative filtering (CF). The SA was performed using an LSTM-based model. On the basis of CF, two distinct recommendation systems were built. The proposed SA model was integrated with the best recommendation system to enhance the recommendations. The experimental findings showed that the proposed system for product recommendation outperformed the existing methods. The outcomes demonstrated the potential of combining CF and SA to improve consumer satisfaction and product recommendation in e-commerce systems. [ABSTRACT FROM AUTHOR]

Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data sparsity problem. This survey paper presents an in-depth analysis of the paradigm shift from conventional recommender systems to generative pre-trained-transformers-(GPT)-based chatbots. We highlight recent developments that leverage the power of GPT to create interactive and personalized conversational agents. By exploring natural language processing (NLP) and deep learning techniques, we investigate how GPT models can better understand user preferences and provide context-aware recommendations. The paper further evaluates the advantages and limitations of GPT-based recommender systems, comparing their performance with traditional methods. Additionally, we discuss potential future directions, including the role of reinforcement learning in refining the personalization aspect of these systems. [ABSTRACT FROM AUTHOR]

Session-aware recommender systems capture user-specific preferences that emerge within multiple user sessions by leveraging the sequential nature of user interactions. Existing session-aware recommendation methods face challenges in finding the right balance between exploration and exploitation leading to less diverse recommendations and also suffering from overestimation bias. This bias problem refers to the tendency for value estimates to be higher than their true values resulting in slower convergence, suboptimal, and less diverse recommendations. This paper proposes a Double Deep Q-network based session-aware recommender system, DDQN-SaRS, which takes care of the overestimation bias and generates diverse recommendations capturing the user's dynamic interests. The proposed system works in two phases. The first phase generates embedding for users, items, and sessions using a Graph Convolutional Network (GCN). The obtained embedding vectors are then given to Double Deep Q-Network (DDQN), a Double Deep Reinforcement Learning (DDRL) technique for suggesting items of interest to the user(s) in the second phase. DDQN decouples the task of action selection and action evaluation by utilizing two networks viz. main and target networks and resolves the overestimation bias problem while maintaining diversity in recommendations. The proposed system learns recommendation policies and corresponding rewards from a pure offline setting. It is validated on two real-world datasets: Diginetica from the CIKM Cup Challenge 2016 and Retailrocket from the Kaggle competition. Experimental results show that our proposed system, DDQN-SaRS outperformed various baseline algorithms viz. S-POP, Item-KNN, GRU4Rec, STAMP, HRNN, NSAR,IDSR, and GNN-GNF. [ABSTRACT FROM AUTHOR]

Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based on the predicted preferences using quality of service data and asymmetrically weighted cosine similarity. To address the cold start problem, we design a questionnaire to survey new user preferences and suggest personalized cloud providers accordingly. The experiments based on the Cloud Armor database demonstrate that our approach outperforms other models. The proposed approach has a precision of 85% and achieves a mean absolute error (MAE) of 0.05 and root-mean-square error (RMSE) of 0.14 for the differences between the input and predicted values. We also receive a satisfaction level of nearly 78.5% for recommendation lists provided to new cloud service customers. [ABSTRACT FROM AUTHOR]

This study introduces a novel movie recommender system utilizing a Semantic-Enhanced Variational Graph Autoencoder for Movie Recommendation (SeVGAER) architecture. The system harnesses additional information from movie plot summaries scraped from the internet, transformed into semantic vectors via a large language model. These vectors serve as supplementary features for movie nodes in the SeVGAER-based recommender. The system incorporates an encoder-decoder structure, operating on a user-movie bipartite graph, and employs GraphSAGE convolutional layers with modified aggregators as the encoder to extract latent representations of the nodes, and a Multi-Layer Perceptron (MLP) as the decoder to predict ratings with additional graph-based features. To address overfitting and improve model generalization, a novel training strategy is introduced. We employ a random data splitting approach, dividing the dataset into two halves for each training instance. The model then generates outputs on each half of the data, and a new loss function is introduced to ensure consistency between these two outputs, a strategy that can be seen as a form of contrastive learning. The model’s performance is optimized using a combination of this new contrastive loss, graph reconstruction loss, and KL divergence loss. Experiments conducted on the Movielens100k dataset demonstrate the effectiveness of this approach in enhancing movie recommendation performance. [ABSTRACT FROM AUTHOR]

A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collaborative filtering, and content-based filtering, face significant challenges such as data scalability, data scarcity, and the cold-start problem, all of which require advanced solutions. Therefore, we propose a ranking and enhancing sequence movie recommendation system that utilizes the combination model of deep learning to resolve the existing issues. To mitigate these challenges, we design an RSs model that utilizes user information (age, gender, occupation) to analyze new users and match them with others who have similar preferences. Initially, we construct sequences of user behavior to effectively predict the potential next target movie of users. We then incorporate user information and movie sequence embeddings as input features to reduce the dimensionality, before feeding them into a transformer architecture and multilayer perceptron (MLP). Our model integrates a transformer layer with positional encoding for user behavior sequences and multi-head attention mechanisms to enhance prediction accuracy. Furthermore, the system applies KMeans clustering to movie genre embeddings, grouping similar movies and integrating this clustering information with predicted ratings to ensure diversity in the personalized recommendations for target users. Evaluating our model on two MovieLens datasets (100 Kand 1 M) demonstrated significant improvements, achieving RMSE, MAE, precision, recall, and F1 scores of 1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 100 K dataset, and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset, respectively. This approach not only effectively mitigates cold-start and scalability issues but also surpasses baseline techniques in Top-N item recommendations, highlighting its efficacy in the contemporary environment of abundant data. [ABSTRACT FROM AUTHOR]

Floor localization is crucial for various applications such as emergency response and rescue, indoor positioning, and recommender systems. The existing floor localization systems have many drawbacks, like low accuracy, poor scalability, and high computational costs. In this paper, we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a subgraph. Then, we introduce FloorLocator, a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural networks. This approach offers high accuracy, easy scalability to new buildings, and computational efficiency. Experimental results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art methods. Notably, in building B0, FloorLocator achieved recognition accuracy of 95.9%, exceeding state-of-the-art methods by at least 10%. In building B1, it reached an accuracy of 82.1%, surpassing the latest methods by at least 4%. These results indicate FloorLocator's superiority in multi-floor building environment localization. [ABSTRACT FROM AUTHOR]

In recent times, recommender systems have acquired significant popularity as a solution to the issue of information overload. These systems offer personalised recommendations to users. The superiority of multi-criteria recommender systems over their single-criterion counterparts has been demonstrated, as the former are able to provide more precise recommendations by taking into account multiple dimensions of user preferences when rating items. The prevalent recommendation technique, matrix factorization of collaborative filtering, is hindered by the data sparsity problem of the user-item matrix. On the other hand, it is noteworthy that deep learning techniques have demonstrated significant potential in various research domains, including but not limited to image processing, pattern recognition, computer vision, and natural language processing. In recent times, there has been a surge in the utilisation of deep learning techniques in recommender systems, yielding promising outcomes. This study presents a novel approach to multi-criteria recommender systems through the utilisation of deep learning algorithms to mitigate the data sparsity issue. Specifically, deep autoencoders are utilised to uncover complex, non-linear, and latent relationships between users' multi-criteria preferences followed by matrix factorization technique, ultimately leading to more precise recommendations. The proposed model is evaluated by conducting the experiments on the multi-criteria dataset of Yahoo! Movies. According to the outcomes, the proposed approach outperforms the state of the art recommendation methods by generating more accurate and personalized recommendations. Also, it reduces the data sparsity up to 11% from the multi-criteria dataset. [ABSTRACT FROM AUTHOR]

This paper proposes an explainable recommendation algorithm based on deep learning for developing a transparent and explainable recommendation system. The proposed algorithm combines a multi-feature fusion model for text sentiment analysis and the innovative DeepxDeepFM recommendation model to provide accurate and interpretable recommendations. First, Bi-LSTM and MCNN are employed to extract multi-dimensional features from comment data. Then, the DeepxDeepFM recommendation model is used explicitly for comment data with numerous feature vectors and significant linear changes. Finally, experimental results demonstrate that our proposed explainable recommendation algorithm increases the accuracy by 1.57% and decreases the root mean square error by 2.69%, contributing to higher model performance. Compared to other models, the improved interpretable recommendation model is smaller in size and more accurate, so it can maximize the click-through rate of e-commerce recommendation systems, which is crucial for achieving precise recommendations in the field of e-commerce. [ABSTRACT FROM AUTHOR]

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning Recommender Systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. Users posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. Innovative has been made in this work to propose a new e-learning recommendation system based on individualization and FSA. A new framework is proposed based on collaborative filtering models (CFMs) integrating with fine-grained sentiment analysis (FSA) for hybrid recommendation (CFISAR) for effective recommendations. CFMs attempt to capture the learner's latent factors based on their selections of interest to build the learner profile. FSA models are introduced to deliver e-content with the highest ranked ratings related to the learner's area and interests based on the extracted learner model. Moreover, a new approach is proposed to update the system continuously and not keep it bound to certain items by adding new books, where the initial rating of these new books is predicted based on FSA models. CFISAR is explored utilizing six CFMs to generate the prediction matrix and derive the learner model, resulting in a low MSE of 0.699 for Asymmetric SVD. The system used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context, and leveraging the goodness of deep learning, which predicted an accuracy of 0.9264% for the Peephole algorithm, that performed better than other models. [ABSTRACT FROM AUTHOR]

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Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN), AdaBoost, and Artificial Immune Recognition System v2, highlights the superior accuracy of the proposed GRU model, which achieved an impressive overall accuracy of 99.70%. This breakthrough underscores the model's potential in aiding educational institutions to proactively support students, thereby mitigating the risks of underachievement and dropout. [ABSTRACT FROM AUTHOR]

Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques. [ABSTRACT FROM AUTHOR]

Emotion detection from text plays a very critical role in different domains, including customer service, social media analysis, healthcare, financial services, education, human-to-computer interaction, psychology, and many more. Nowadays, deep learning techniques become popular due to their capabilities to capture inherent complex insights and patterns from raw data. In this paper, we have used the Word2Vec embedding approach that takes care of the semantic and contextual understanding of text making it more realistic while detecting emotions. These embeddings act as input to the convolution neural network (CNN) to capture insights using feature maps. The Word2Vec and CNN models applied to the international survey on emotion antecedents and reactions (ISEAR) dataset outperform the models in the literature in terms of accuracy and F1-score as model evaluation metrics. The proposed approach not only obtains high accuracy in emotion detection tasks but also generates interpretable representations that contribute to the understanding of emotions in textual data. These findings carry significant implications for applications in diverse domains, such as social media analysis, market research, clinical assessment and counseling, and tailored recommendation systems. [ABSTRACT FROM AUTHOR]

In the domain of data-centric networks, Link Prediction (LP) is instrumental in discerning potential or absent connections among entities within complex networks. By employing graph data structures, LP techniques enable a detailed analysis of entity interactions across varied sectors, contributing significantly to overcoming challenges in data filtering and integrity restoration, primarily when the network does not provide embedded data. Although LP methods are widely applicable, especially in recommender systems, their efficacy in current social networks needs to be thoroughly investigated. This study introduces an innovative LP approach using Deep Neural Networks (DNNs). We compare our method against a comprehensive set of established techniques, including traditional score-based methods, classical baselines, and recent deep learning approaches like Graph Neural Networks (GNNs). Our DNN-based solution incorporates a robust feature extraction process and a binary classifier, optimized for accurate prediction of missing links within networks. We performed extensive experimental evaluations on diverse datasets, including co-authorship networks, e-commerce, and social media networks. The study encompasses a comparative analysis with traditional LP techniques, namely Common Neighbors, Resource Allocation Index, Jaccard’s Coefficient, and Adamic/Adar Index, as well as other selected baseline and deep-learning methods. Our findings demonstrate that the DNN-based approach significantly enhances predictive accuracy, outperforming the conventional baseline methods in link prediction.

The article discusses the problem of information overload caused by the rapid development of the Internet and the large amount of available electronic resources. To address this issue, recommender systems are used to filter and classify data based on user behavior and interests. The article highlights various algorithms used in recommender systems, such as content-based systems, collaborative filtering, and hybrid recommender systems. It also introduces a special issue on new trends in algorithms for intelligent recommendation systems, featuring seven research articles that cover topics such as artificial general recommenders, sequential pattern-based e-commerce recommendation systems, addressing the cold-start problem, hybrid recommender systems for the tourism domain, managing natural noises in recommender systems, using graph neural networks for social recommendations, and information retrieval and machine learning methods for academic expert finding. [Extracted from the article]

Abstract The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we investigate the effectiveness of three different embedding aggregation strategies in the area of drug–target interaction (DTI) prediction. We formally define these strategies and prove their universal approximator capabilities. We then present experiments that compare the different strategies on benchmark datasets from the area of DTI prediction, showcasing conditions under which specific strategies could be the obvious choice.

In an era where digital information is abundant, the role of recommender systems in navigating this vast landscape has become increasingly vital. This study proposes a novel deep learning-based approach integrating multi-context and multi-criteria data within a unified neural network framework. The model processes these dimensions concurrently, significantly improving the precision of personalized recommendations. Context-aware and multi-criteria recommender systems extend traditional two-dimensional user-item preference methods with context awareness and multiple criteria. In contrast to traditional methods, our approach intricately weaves together multi-context and multi-criteria data within its architecture. This concurrent processing enables sophisticated interactions between context and criteria, enhancing recommendation accuracy. While context-aware systems incorporate contextual information such as time and location when making recommendations, multi-criteria-based approaches offer a spectrum of evaluative criteria, enriching the user experience with more tailored and relevant suggestions. Although both approaches have advantages in producing more accurate and personalized referrals, context information and multi-criteria ratings have not been employed together for producing recommendations. Our research proposes a novel deep learning-based approach for the multi-context, multi-criteria recommender system to address this gap. In contrast to traditional approaches that process context-aware recommender systems and multi-criteria recommender systems separately, our deep learning model intricately weaves together multi-context and multi-criteria data within its architecture. This integration is not staged; both dimensions are concurrently processed through a unified neural network framework. The model facilitates a sophisticated interaction between context and criteria by embedding these elements into the core of the network’s multiple layers. This methodology enhances the system’s adaptability and significantly improves its precision in delivering personalized recommendations, leveraging the compounded effects of contextual and criteria-specific insights. The proposed model shows superior performance in predictive tasks, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) on the TripAdvisor and ITMRec datasets compared to other state-of-the-art recommendation techniques. Context-aware multi-criteria ratings data demonstrate the robustness and accuracy of the model.

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance. [ABSTRACT FROM AUTHOR]

The overwhelming burden on the healthcare system has resulted in increased mortality rates as individuals struggle to receive appropriate medical attention. Machine learning has demonstrated its efficacy across diverse applications, fueling a growing trend in the realm of innovative automation. This is particularly facilitated by drug recommendation systems, which possess the capability to significantly reduce the dependence on specialized assistance. Faced with limited access to medical expertise, a considerable number of individuals have turned to self-medication, often worsening their health conditions. In response to this critical scenario, this paper introduces an inventive drug recommendation system which helps to lessen the strain healthcare professionals. Here, we present the development of a medication recommendation system based on LSTM, utilizing user-provided symptoms and the model's evaluations of past drug recommendations. The results of the comparison between Random Forest, SVM, and LSTM indicated that the modified LSTM-based recommendation system outperforms the other two models, achieving a 94% accuracy rate. [ABSTRACT FROM AUTHOR]

Sentiment analysis is critical for classifying users on social media and reviewing products through comments and reviews. At the same time, rating prediction is a popular and valuable topic in research on recommendation systems. This study improves the accuracy of ratings in recommendation systems through the combination of rating prediction and sentiment analysis from customer reviews. New ratings have been generated based on original ratings and sentiment analysis. Experimental results show that in almost all cases, revised ratings using a deep learning-based algorithm called LightGCN on 7 various real-life datasets improve rating prediction. In particular, rating prediction metrics (RMSE and MAE, R2, and explained variance) of the proposed approach (with revised ratings) are better than those of the typical approach (with unrevised ratings). Furthermore, evaluating ranking metrics (also top-k item recommendation metrics) for this model also shows that our proposed approach (with revised ratings) is much more effective than the original approach (with unrevised ratings). Our significant contribution to this research is to propose a better rating prediction model that uses a supplement factor sentiment score to enhance the accuracy of rating prediction. [ABSTRACT FROM AUTHOR]

Rapid digital content growth demands pivotal font selection for design and communication. Our study focuses on a font recommendation system that aligns fonts with content emotions. To achieve this, we define font-emotions and quantify them. Additionally, we leverage deep learning techniques for content analysis. Understanding common emotional perceptions, we aimed to align fonts with content emotions. After evaluating diverse mapping methods, we determined a correlation analysis-based model to be most effective. Implementing this model, we verified its utility through usability evaluations. Our proposed system not only assists users with limited design knowledge in receiving contextually fitting font suggestions but also extends its application across various digital content realms. [ABSTRACT FROM AUTHOR]

The proliferation of uncategorized information on the Internet has intensified the need for effective recommender systems. Recommender systems have evolved from content-based filtering to collaborative filtering and, most recently, to deep learning-based and hybrid models. However, they often face challenges such as high computational costs, reduced reliability, and the Cold Start problem. We introduce a persona-based user modeling approach for real-time movie recommendations. Our system employs Non-negative Matrix Factorization (NMF) and Deep Learning algorithms to manage complex and sparse data types and to mitigate the Cold Start issue. Experimental results, based on criteria involving 50 topics and 35 personas, indicate a significant performance gain. Specifically, with 500 users, the precision@K for NMF was 86.01%, and for the Deep Neural Network (DNN), it was 92.67%. Tested with 900 users, the precision@K for NMF increased to 97.04%, and for DNN, it was 95.55%. These results represent an approximate 10% and 5% improvement in performance, respectively. The system not only delivers fast and accurate recommendations but also reduces computational overhead by updating the model only when user personas change. The generated user personas can be adapted for other recommendation services or large-scale data mining. [ABSTRACT FROM AUTHOR]

Currently, generative applications are reshaping different fields, such as art, computer vision, speech processing, and natural language. The computer science personalization area is increasingly relevant since large companies such as Spotify, Netflix, TripAdvisor, Amazon, and Google use recommender systems. Then, it is rational to expect that generative learning will increasingly be used to improve current recommender systems. In this paper, a method is proposed to generate synthetic recommender system datasets that can be used to test the recommendation performance and accuracy of a company on different simulated scenarios, such as large increases in their dataset sizes, number of users, or number of items. Specifically, an improvement in the state-of-the-art method is proposed by applying the Wasserstein concept to the generative adversarial network for recommender systems (GANRS) seminal method to generate synthetic datasets. The results show that our proposed method reduces the mode collapse, increases the sizes of the synthetic datasets, improves their ratings distributions, and maintains the potential to choose the desired number of users, number of items, and starting size of the dataset. Both the baseline GANRS and the proposed Wasserstein-based WGANRS deep learning architectures generate fake profiles from dense, short, and continuous embeddings in the latent space instead of the sparse, large, and discrete raw samples that previous GAN models used as a source. To enable reproducibility, the Python and Keras codes are provided in open repositories along with the synthetic datasets generated to test the proposed architecture (https://github.com/jesusbobadilla/ganrs.git). [ABSTRACT FROM AUTHOR]

Tourists share opinions about Points of Interest (POIs) through online posts and social media platforms. Opinion mining is a popular technique for extracting feedback from tourists who visited various places hidden in reviews, which are used in several tourist applications that generally reflect their preference towards POI. On the other hand, a trip schema is difficult for tourists because they must pick up sequential POIs in unknown areas that meet their limitations and preferences. However, most prior trip suggestion methods are suboptimal for several reasons, including that they do not consider valuable user reviews and rely exclusively on left-to-right unidirectional discovery sequence models. This study proposes a Neural Network-Long Short-Term Memory (LSTM) POI recommendation system for calculating user similarity based on opinions and preferences. In addition, it presents a method for discovering sequential trip recommendations with Bidirectional Encoder Representations from Transformer (BERT) using a deep learning method. Furthermore, this neural hybrid framework identifies a list of optimal trip candidates by combining personalized POIs with multifaceted context. Furthermore, this method employs the valuable information contained in user posts and their demographic information on social media to mitigate the well-known cold start issue. In the experimental evaluation based on two datasets, Tripadvisor and Yelp, this hybrid method outperforms other state-of-the-art methods when considering F-Score, nDCG, RMSE, and MAP. [ABSTRACT FROM AUTHOR]

Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. Specifically, HFENet contains two key modules: a hybrid-scale feature extraction block (HFEB) and a shuffle attention enhancement block (SAEB). HFEB is designed to excavate spectral–spatial structure information of distinct scales, types, and branches, which can augment the multiplicity of spectral–spatial features while modeling the global long-range dependencies of spectral–spatial informative features. SAEB is devised to adaptively recalibrate spectral-wise and spatial-wise feature responses to generate the purified spectral–spatial information, which effectively filters redundant information and noisy pixels and is conducive to enhancing classification performance. Compared with several sophisticated baselines, a series of experiments conducted on three public hyperspectral datasets showed that the accuracies of OA, AA, and Kappa all exceed 99%, demonstrating that the presented HFENet achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or items. Addressing the challenge of providing personalized recommendations in the cold start scenario is crucial. This research proposes a new hybrid recommender system named CoDFi-DL which combines demographic and enhanced collaborative filtering. The demographic filtering is performed through a deep neural network (DNN) and used to solve the new user cold start problem. The enhanced collaborative filtering component of our model focuses on delivering personalized recommendations through a neighborhood-based method. The major contribution in this research is the DNN-based demographic filtering which overcomes the new user cold start problem and enhances the collaborative filtering process. Our system significantly improves the relevancy of the recommendation task and thus provides personalized recommended items to cold users. To evaluate the effectiveness of our approach, we conducted experiments on real multi-label datasets, 1M and 100K MovieLens. CoDFi-DL recommender system showed higher performance in comparison with baseline methods, achieving lower RMSE rates of 0.5710 on the 1M MovieLens dataset and 0.6127 on the 100K MovieLens dataset. [ABSTRACT FROM AUTHOR]

Aim/Purpose This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user's characteristics to predict other centers of interest. Contribution This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendations for Researchers Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work. [ABSTRACT FROM AUTHOR]

Deep learning technology has greatly propelled the development of intelligent and information-driven research on ship infrared automatic target recognition (SIATR). In future scenarios, there will be various recognition models with different mechanisms to choose from. However, in complex and dynamic environments, ship infrared (IR) data exhibit rich feature space distribution, resulting in performance variations among SIATR models, thus preventing the existence of a universally superior model for all recognition scenarios. In light of this, this study proposes a model-matching method for SIATR tasks based on bipartite graph theory. This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. The objective is to selectively recommend the optimal candidate model for a given sample, enhancing the overall recognition performance and applicability of the model. We separately conducted tests for the optimization of accuracy and credibility on high-fidelity simulation data, achieving Accuracy and EDMS (our credibility metric) of 95.86% and 0.7781. Our method improves by 1.06% and 0.0274 for each metric compared to the best candidate models (six in total). Subsequently, we created a recommendation system that balances two tasks, resulting in improvements of 0.43% (accuracy) and 0.0071 (EDMS). Additionally, considering the relationship between model resources and performance, we achieved a 28.35% reduction in memory usage while realizing enhancements of 0.33% (accuracy) and 0.0045 (EDMS). [ABSTRACT FROM AUTHOR]

General video search and recommendation systems primarily rely on metadata and personal information. Metadata includes file names, keywords, tags, and genres, among others, and is used to describe the video's content. The video platform assesses the relevance of user search queries to the video metadata and presents search results in order of highest relevance. Recommendations are based on videos with metadata judged to be similar to the one the user is currently watching. Most platforms offer search and recommendation services by employing separate algorithms for metadata and personal information. Therefore, metadata plays a vital role in video search. Video service platforms develop various algorithms to provide users with more accurate search results and recommendations. Quantifying video similarity is essential to enhance the accuracy of search results and recommendations. Since content producers primarily provide basic metadata, it can be abused. Additionally, the resemblance between similar video segments may diminish depending on its duration. This paper proposes a metadata expansion model that utilizes object recognition and Speechto-Text (STT) technology. The model selects key objects by analyzing the frequency of their appearance in the video, extracts audio separately, transcribes it into text, and extracts the script. Scripts are quantified by tokenizing them into words using text-mining techniques. By augmenting metadata with key objects and script tokens, various video content search and recommendation platforms are expected to deliver results closer to user search terms and recommend related content. [ABSTRACT FROM AUTHOR]

Copy-move forgery detection poses a significant challenge because the brightness and contrast of forged and real regions are highly consistent, and the forensic clues are weakened by various affine transformations and post-processing operations. Existing deep learning models ignore the hierarchical dependencies of the features and pay less attention to information purification. To solve these problems, we propose a novel two-stage model to detect copy-move forgery, dubbed by CCAIR-Net. First, in the coarse localization stage, we propose a cross-scale condition aggregation module to integrate multi-level features from global to local. This aggregation structure can eliminate semantic disparities and capture multi-scale hierarchical dependencies of features. In particular, the condition aggregation block enables feature purification and filtering of interfering information. Second, in the refinement stage, the designed weighted fusion mechanism can guide the model to remove falsely detected regions and supplement the miss-detected regions by adaptively weighted fusion of coarse-grained and fine-grained features. Furthermore, the stage-wise training strategy takes advantage of different losses to train the network to detect tampered regions at various scales. Extensive experiments demonstrate that the proposed CCAIR-Net performs better than state-of-the-art methods. It can detect and segment forged and real areas more accurately, even for affine transformations and post-processing attack images. [ABSTRACT FROM AUTHOR]