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With the advancement of Internet, recommendation systems have become an indispensable component for every e-commerce platform, playing an increasingly pivotal role in product recommendations. However, due to the recommendation mechanisms of these systems, numerous new attack patterns have emerged. A novel attack pattern targeting e-commerce recommendation systems, termed the "Ride Item's Coattails" attack, fabricates false click information to deceitfully establish associations between popular products and low-quality products, intending to mislead the e-commerce platform's recommendation system and promote the sales of substandard products. This article presents a recommendation system attack detection method based on the Deep Forest algorithm to address the challenges of these novel recommendation system attacks. Random forests are used for feature selection, aiming to filter crucial features and reduce feature redundancy. To tackle the issue of extreme class imbalance, a symmetric sampling technique based on k-means centroids is introduced. This approach addresses the incomplete noise filtering and sampling data comprehensiveness issues commonly found in undersampling algorithms. Considering the potential for even more imbalanced data in real-world scenarios, a combined strategy of undersampling and SMOTE resampling is used to handle imbalanced data. The proposed algorithm was trained on e-commerce "Ride Item's Coattails" attack identification data from Alibaba Cloud's Tianchi, which originates from genuine recommendation system attack data. The proposed method was compared with various deep learning and machine learning algorithms, such as DLMP and deep neural networks (DNN), for extensive validation. Experiments demonstrate that the proposed technique effectively meets the demands for attack detection. [ABSTRACT FROM AUTHOR]

The advancements in intelligent vehicle technologies are facilitating the growth of information technology (IT) platforms, unlike conventional automobiles. In-vehicle-infotainment (IVI) is becoming an appealing element in intelligent vehicles as it offers various experiences to users; however, it requires personalized services to provide even more sophisticated user experiences. It is supposed that passengers search for businesses that provide products or services they found interesting in videos played via IVIs while the vehicle is driving autonomously. In that case, it could be more effective to use images that can express the user's preference as a query for the search than to utilize texts such as product names. Accordingly, this study proposes a recommendation system that informs users of businesses near an intelligent vehicle when a passenger inputs an image of a product or service into an IVI system. The proposed recommendation system involves training deep learning-based image classification models with the user's interest images to classify the category, measure the similarity with the business category using Word2vec, and finally provide the locations of the businesses with a high degree of similarity via IVI, using a smartphone. The experimental results indicated that the user's interest image exhibited 85% accuracy for category classification via the EfficientNet B0 model, while the similarity between the image and business categories using Word2vec was particularly high in the business category similar to the actual image category. [ABSTRACT FROM AUTHOR]

Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners' current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric-based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large-scale learning data. While the deep learning-based learning diagnosis methods have improved the accuracy of learning performance prediction, their inherent black-box properties lead to a lack of interpretability, making their results untrustworthy for educational applications. To settle the abovementioned problem, the proposed unified interpretable intelligent learning diagnosis framework, which benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics, achieves a better performance of learning prediction and provides interpretability from three aspects: cognitive parameters, learner-resource response network, and weights of self-attention mechanism. Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI. Experiments on two real-world datasets and a simulation dataset show that our method has higher accuracy in predicting learners' performances compared with the state-of-the-art models and can provide valuable educational interpretability for applications such as precise learning resource recommendation and personalized learning tutoring in intelligent tutoring systems. [ABSTRACT FROM AUTHOR]

With the development of information technology, various cloud music services are gradually emerging, which has fully changed and enriched people's music life. How to propose the songs that consumers anticipate from the enormous song data is one of the key goals of the music recommendation system. This research aims to create a better music algorithm that incorporates user data for deep learning, a candidate matrix compression technique for suggestion improvement, accuracy, recall rate, and other metrics as evaluation criteria. In terms of recommendation methods, the music-music recommendation method based on predicting user behavior data and the recommendation method based on automatic tag generation are proposed. The music features obtained by audio processing are fully utilized, and the depth content information in music audio data is combined with other data for recommendation, which improves the tag quality and avoids the problem of low coverage. The results show that this model can extract the effective feature representation of songs in different classification criteria and achieve a good classification effect simultaneously. [ABSTRACT FROM AUTHOR]

Due to the huge system in the field of constitution, the content of cases is complicated, and the number of documents related to constitutional cases is huge. It is incredibly tough to find the information that suits your demands in such a large volume of constitutional documents. The artificial neural network-based deep learning method has made significant progress. Deep learning has been used in the disciplines of computer image and video processing, as well as natural language processing, and has outperformed typical machine learning methods. To that purpose, the content-based recommendation algorithm in this work is improved using deep learning approaches. This paper primarily covers the following topics: (1) the current research status of deep learning technology in recommender systems is reviewed, and the theoretical knowledge of deep neural networks is introduced. (2) A text classification model OCCNN based on character-level convolutional neural network is proposed to solve the problem of document classification of massive cases. A semantic similarity calculation model TF-W2V based on word frequency and word vector is also proposed to solve the problem of semantic similarity calculation of cases. (3) The two methods are tested, and the experimental results show that the accuracy is much higher than that of the conventional method. The model can recommend cases that meet the needs of users according to the requirements put forward by users in the process of handling cases. [ABSTRACT FROM AUTHOR]

With the arrival of graduation season, the number of graduates is expanding every year, and the employment rate of college students has become one of the data that colleges and universities pay attention to. To improve the employment rate of college students, we should first educate each college student in career planning, so that they can clearly understand their own positioning and future development direction. Career planning can guide students to understand their jobs and analyze their professional fields. Faced with massive employment information, it is also a big problem for college students to search and choose information. In this paper, the personalized recommendation system for entrepreneurship is described, and the basic principles of information recommendation are described. The basic information and personal interest points of college students are represented by feature vectors, which provide a favorable theoretical support for college students' career planning and employment and entrepreneurship information recommendation. An information recommendation model under deep learning is formed. Finally, the performance of the model under the traditional algorithm and the optimized information recommendation model is evaluated, and the satisfaction of users to this system is scored so as to provide a convenient and quick information recommendation system for college students, thus indirectly improving the employment rate of graduates and providing corresponding solutions to the problem of difficult employment. [ABSTRACT FROM AUTHOR]

Graph convolutional network (GCN)-based recommendation has recently attracted significant attention in the recommender system community. Although current studies propose various GCNs to improve recommendation performance, existing methods suffer from two main limitations. First, user-item interaction data is generally sparse in practice, highlighting these methods' ineffectiveness in learning user and item feature representations. Second, they usually perform a dot-product operation to model and calculate user preferences on items, leading to inaccurate user preference learning. To address these limitations, this study adopts a design idea that sharply differs from existing works. Specifically, we introduce the knowledge distillation concept into GCN-based recommendation and propose a two-phase knowledge distillation model (TKDM) improving recommendation performance. In Phase I, a self-distillation method on a graph auto-encoder learns the user and item feature representations. This auto-encoder employs a simple twolayer GCN as an encoder and a fully connected layer as a decoder. On this basis, in Phase II, a mutual-distillation method on a fully connected layer is introduced to learn user preferences on items with triple-based Bayesian personalized ranking. Extensive experiments on three realworld data sets demonstrate that TKDM outperforms classic and state-of-the-art methods related to GCN-based recommendation problems. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the recommendation system is becoming more and more important in people's life. Click-through rate prediction is a crucial task in the recommendation system, which directly determines the effect of the recommendation system. Recently, researchers have found that considering the user behavior sequence can greatly improve the accuracy of the click-through rate prediction model. However, the existing prediction models usually use the user click behavior sequence as the input of the model, which will make it difficult for the model to obtain a comprehensive user interest representation. In this paper, a unified multitype user behavior sequence modeling framework named as MBIN, a.k.a. multifeedback behavior-based Interest modeling network, is proposed to cope with uncertainties in the noisy data. The proposed adaptive model uses deep learning technology, obtains user interest representation through multihead attention, denoises user interest representation using the vector projection method, and fuses the user interests using adaptive dropout technology. First, an interest denoising layer is proposed in the MBIN, which can effectively mitigate the noise problem in user behavior sequences to obtain more accurate user interests. Second, an interest fusion layer is introduced so as to effectively model and fuse various types of interest representations of users to achieve personalized interest fusion. Then, we used auxiliary losses based on behavior sequences to enhance the effect of behavior sequence modeling and improve the effectiveness of user interest characterization. Finally, we conduct extensive experiments based on real-world and large-scale dataset to validate the effectiveness of our approach in CTR prediction tasks. [ABSTRACT FROM AUTHOR]

In order to provide tourists with better tourism services, a system method of personal information recommendation platform based on deep learning tourism is proposed. The system includes noise reduction autoencoder, feature extraction module, data preprocessing module, recommendation calculation module, expert evaluation module, recommendation result output module, customer feedback module, and storage module. The personal information recommendation platform system based on deep learning tourism of the present invention enables tourists to obtain tourism information conveniently and quickly through scientific information organization and presentation form and helps tourists to better arrange tourism plans and form tourism decisions. By effectively aggregating multiple neighborhoods of nodes, embedding high-order collaboration information into the node embedding vector, obtaining the potential preferences of users, solving the problems of user data sparse and cold start, and finally through experimental analysis, a research method is proposed. It is used to build the model of tourist attraction recommendation system. Experimental results show that the proposed method for cold-start user recommendation has the best performance in terms of accuracy, recall, and normalized loss cumulative gain, and it is 17.9% higher than BPR in recall rate Recall@5 and 11.8% higher in accuracy rate. It is proved that the system has a significant impact on the diversity and novelty of tourist attraction recommendation. [ABSTRACT FROM AUTHOR]

In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D -dimensional characteristics from the source face image, followed by the feature selection strategy. The face's distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person's age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time. [ABSTRACT FROM AUTHOR]

Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm. Moreover, the clothing style recognition model based on a multilabel classification algorithm and the clothing recommendation system based on a decision tree algorithm are tested by building simulation experiments and combining neural network technology. Finally, the application of the decision tree algorithm and DL theory in clothing recommendation design is studied through the literature collection method. The research focus is to realize the recognition of clothing through decision tree algorithm and DL method to achieve the intelligent recommendation of clothing style. The results show that: (1) the neural network technology in DL theory can realize efficient recognition and classification of clothing style by automatically extracting image features and combining with a multilabel classification algorithm. (2) The decision tree algorithm can make an initial recommendation according to users' style preferences, then make implicit recommendations through user retrieval, browsing, and other operations, and make dynamic clothing style recommendations to users. (3) When the neural network based on a multilabel classification algorithm is trained, the precision, recall rate, and F1 values are 0.73, 0.43, and 0.55, respectively. (4) After using the clothing recommendation system based on the decision tree algorithm, the subjects' average satisfaction is 86.25%, indicating that this system can give users a better clothing recommendation experience. This exploration aims to provide a crucial reference for further improving the quality of clothing recommendation services. It has important theoretical significance and practical value for the development of artificial intelligence in the field of fashion design, and is expected to provide a reference for the development of bionics. [ABSTRACT FROM AUTHOR]

With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resource information, such as talent information and job information, has also increased unprecedentedly, which makes human resource services face information overload. Especially with the gradual increase of the amount of information, this method is not enough for the acquisition and classification of massive data. After that, experts developed search engines to deal with the retrieval problem, and the first ones were Google and Baidu. As long as the search engine is clear about the direction of the search, it is indeed very convenient for the retrieval of massive data. However, in many cases, most users cannot clearly recognize the content they need or how to accurately express their needs. Faced with this problem, people propose recommender systems to solve the problem of obtaining preference information, which can better increase the user's experience and meet their own needs more easily. Based on the main workflow of the recommender system, this paper designs the overall architecture of the human resources recommendation system and implements a human resources recommendation prototype system based on deep learning. The system can better overcome the cold start problem and provide real-time recommendation results, improving the quality of HR personalized recommendation results. [ABSTRACT FROM AUTHOR]

With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resources information such as talent information and postinformation has also increased unprecedentedly, which makes human resources services face the problem of information overload. At the same time, deep learning has achieved great success in a series of fields such as computer vision, natural language processing, and semantic recognition in recent years. However, there are few related works in the field of deep learning applied to human resource management system at present. Therefore, this paper studies and improves the recommendation algorithm based on deep learning and applies it to the field of human resources recommendation. In order to improve the traditional and single algorithm of the existing recommendation system, and improve the performance of the human resource management recommendation system. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of science and Internet technology, people's lifestyles are changing dramatically, especially with the development of information technology, which has contributed to the transformation of digital libraries. As an essential information infrastructure and a new source of knowledge, digital libraries have brought great convenience to users. To be specific, with the widespread use of smart devices and internet of things technology, users are eager to be intelligent in their information needs while enjoying services, which makes the resource recommendation service of digital libraries increasingly important. In addition, as a provider of knowledge and information services, libraries should organically combine advanced information technology with existing resources to promote the construction of libraries in the information age. However, in the era of big data, users can only passively receive a large amount of information and services in the face of the ever-expanding mass of resources in digital libraries. In this context, libraries might only provide a single set of information resources and services, which cannot meet the individual needs of users and ultimately leads to inefficient allocation of resources and information. After all, users of digital libraries want to be better able to receive personalized recommendations for library resources through relevant technologies. At the same time, libraries are increasing their research and development efforts on algorithms and technologies for personalized recommendations. Also, with the explosive growth of the total amount of information worldwide, people are entering the information age. Massive amounts of data are constantly being generated, and the problem of information overload is becoming more and more serious. The sheer volume of this data and information increases the degree of difficulty in accessing the information people need. In this situation, it is necessary for digital libraries to dynamically analyze user behavior and interests while responding to user requests in a timely manner and accordingly take the initiative to recommend information resources and knowledge services that meet users' individual needs. As a result, this study uses a deep belief network model for multimodal feature learning and designs a personalized recommendation system for library resources by fusing features from multiple modalities. Furthermore, this research implements the construction of a semantic user interest model and the design of a personalized recommendation algorithm to achieve an accurate description of user interest preferences and semantic personalized recommendation functions. [ABSTRACT FROM AUTHOR]

Research studies have shown that online recommender systems are under the threat of group shilling attacks, in which attackers attempt to distort the recommendation results of particular items by cooperatively injecting fake profiles. Existing detection methods usually divide candidate groups at first and then use the hand-crafted features to recognize shilling groups. However, the detection performance of existing methods depends highly on the quality of candidate groups obtained. Moreover, extracting features manually is time-consuming. To overcome these limitations, we propose a shilling group detection framework based on the sparse autoencoder and modified GraphSAGE model. First, we use the sparse autoencoder to obtain rating features of users from the rating dataset. Second, we analyse user collusive degrees to calculate user transition probabilities. Third, we build a user relation graph and utilize the modified GraphSAGE model to perform user classification. Finally, shilling groups are gathered according to the neighbour relations. Extensive experiment demonstrates that the proposed framework performs better on different datasets than baseline methods. [ABSTRACT FROM AUTHOR]

Accurate recommendation of tourist attractions is conducive to improving users' travel efficiency and tourism experience. However, the choice of tourism feature factors and the difference of recommendation algorithm will affect the accuracy of scenic spot recommendation. Aiming at the problems of sparse data, insufficient tourism factors, and low recommendation accuracy in the existing tourism recommendation research, this paper puts forward a scenic spot recommendation method based on microblog data and machine learning by using the characteristics of personalized expression and strong current situation of microblog data and the intelligent prediction function of machine learning, so as to realize accurate and personalized scenic spot recommendation. This paper extracts rich tourism characteristic factors. Typical tourism recommendation algorithms choose tourism characteristic factors from scenic spots, tourists, and other aspects, without considering the travel time, tourism season, and other contextual information of tourists' destination, which can help understand users' tourism preferences from different angles. Aiming at the problem of sparse data and cold start of collaborative filtering recommendation algorithm, this paper introduces deep learning algorithm and combines the proposed multifeature tourism factors to build dynamic scenic spot prediction models (random forest preferred attraction prediction (RFPAP) and neural networks preferred attraction prediction (NNPAP)). The experimental results show that RFPAP and NNPAP methods can overcome the problem of data sparsity and achieve 89.61% and 89.51% accuracy, respectively. RFPAP method is better than NNPAP method and has stronger generalization ability. [ABSTRACT FROM AUTHOR]

It is one of the main goals of personalized music recommendation system that how to accurately recommend the songs in line with users' interests in the huge music library. In view of the above problems, this study proposes a personalized music recommendation method based on convolutional neural network. First, this study defines a training set containing potential musical characteristics and, combined with the depth of the belief network, design a music information prediction model and the research in the music-type classification method with different dimensions. Based on selecting four different kinds of music information better describing the underlying characteristics of 40D feature vector to every song music composition, the music feature set is constructed. Then, the CNN (convolutional neural network), which is widely used in audio field, is used as the music information prediction model, and its structural parameters are redesigned to complete the multidimensional music information prediction, which solves the cold start problem to some extent. [ABSTRACT FROM AUTHOR]

In order to meet the differentiated needs of students and improve the satisfaction of college mental health textbook recommendation, a college mental health textbook recommendation scheme based on improved deep learning algorithm is proposed. Based on the analysis of the principle of deep learning data recommendation system, the data interest is calculated according to the browsing records of students on college mental health textbooks. Combine the deep learning algorithm and collaborative filtering algorithm to collect the demand data of college mental health textbooks, then use the Naive Bayesian classification method to divide the college mental health textbooks into interested and uninterested parts, and recommend the interested college mental health textbooks to the students in need. Experiments show that the longest recommendation time of the college mental health textbook recommendation scheme based on the improved deep learning algorithm proposed in this paper is 4.5 min, the highest recommended recall rate is 95.18%, the average accuracy is 97.2%, the highest content richness is 0.8, the system stability coefficient is 1.06, and the overall average praise rate is 97.79%. It has a good recommendation effect. [ABSTRACT FROM AUTHOR]

With the rapid development of music streaming media service industry, users can easily hear any song on mobile devices. Internet has become a huge music storage platform. With the development of network and large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music loved by users from the massive Internet digital music data has become the key problem and main goal to be solved in the field of music information retrieval. Personalized music recommendation system can accurately find and push songs that users may be interested in from tens of millions of huge music libraries according to users' information under the condition that users only have vague demand for listening to songs. Relying on the traditional search method to find the music that you are interested in can no longer meet the needs of users, so the current music recommendation system needs to dig out the music that has no clear needs in the long tail to help people find their favorite songs. [ABSTRACT FROM AUTHOR]

To improve the recommendation accuracy of educational resources, an intelligent recommendation method based on autoencoder has been proposed by combining intelligent recommendation algorithm and autoencoder. The method uses the dimension reduction advantage of autoencoder to obtain the required feature vector. Then, the prediction score is utilized to recommend educational resources. Finally, it is verified from the algorithmic and system perspective. The results show that this recommendation method is the most efficient method. The efficiency on the dataset is 0.90, respectively. Furthermore, it can score different recommended articles, and the recommendation of different educational resources is realized. [ABSTRACT FROM AUTHOR]

Tourists are the people who can be seen all over the world. Therefore, this has increased the demand for product supply in tourist locations. Technological development would be the only solution to solve those issues related to the demand and supply of the products. Furthermore, a tourist needs enough information about the country he wishes to travel. Some specific data required include hotels, destinations, malls, and tourist places, and they are needed before they land on the tourist country. The data collection can be achieved by applying trending technologies such as deep learning algorithms and some intelligent systems. Furthermore, the tourist may collect information about the locations through feasible devices such as laptops and mobile phones. Among the varying devices and technologies, the most preferred and convenient device should be carried wherever they travel with ease. For example, cellular phones may be considered the easiest modem to carry and use with this specification. In this perspective, the lightweight deep learning model will make significant technology access to resources. Typically, deep learning models are designed with the prospect of extracting the features, potentially to create easy tool access within the mobile accessing service. Visual Bayesian Personalized Ranking (VBPR) Algorithm using DL is implemented for initiating the recommendation system for tourist attractions in any given location. The proposed model was compared with various existing algorithms, and it was found that the proposed system had delivered 98.56% of accurate recommendations for tourist travelers. [ABSTRACT FROM AUTHOR]

On the basis of the analysis of the evolution dynamics and the process of smart tourism service, this paper constructs the evolutionary game model of smart tourism service and reveals the evolution mechanism of smart tourism service based on the network platform. Based on the strategic main line of "advantages," it proposes the design ideas and overall framework of the smart tourism service model based on the network platform, including the smart tourism information interactive service model, the element collaborative service model, and the value cocreation service model. The comparison of recommendation results shows that the recommendation error of the genetically improved generalized regression neural network algorithm is reduced, and the recommendation accuracy is better than that of the unimproved generalized regression neural network algorithm. In the recommendation scenario of click-through rate recommendation, the existing recommendation models are difficult to meet the functions of memory and generalization at the same time and cannot fully mine and combine low-level features, and the model parameters of the deep learning model are difficult to learn under the high-dimensional sparse data set of the recommendation system. To solve the problem of generalization, this paper proposes a deep CTR recommendation model based on the gradient boosting tree and factorization machine. It can fully mine low-level feature information and automatically realize low-level feature combination, which can better learn model parameters on high-dimensional sparse data sets, and the recommendation results are no longer overgeneralized. In this paper, simulation experiments are carried out on the data set, and the related recommendation models are compared. The experimental results show that the model proposed in this paper achieves better results in both the AUC (area under ROC curve) evaluation index and the cross-entropy evaluation index. [ABSTRACT FROM AUTHOR]

The goal of the session-based recommendation system (SBRS) is to predict the user's next behavior based on anonymous sessions. Since long-term historical information of users is not available, deep learning technology has become the mainstream technology in session-based recommendation systems instead of traditional content-based recommendation methods. However, most SBRS methods only consider the session itself, ignoring the collaborative information from other sessions. Even if some SBRS models consider collaborations between sessions, they mostly use the click order to calculate the similarity only and ignore the time the user spends on different items, which might imply the user's varying interest on these items. In this paper, we propose a session-based recommendation model with GNN and time-aware memory networks (SR-GTM), which learns the user's interest representation by combining the information from the session itself and the collaborative information from relevant neighbor sessions. Specifically, SR-GTM mainly includes inner feature extraction module (IFEM) and outer feature extraction module (OFEM). IFEM uses GNN to learn the session features based on its item sequence, and OFEM uses a memory network with dwell time information encoded to extract collaborative information. Finally, SR-GTM aggregates IFEM and OFEM by the gating mechanism and then decodes the output by a softmax layer to obtain the recommendation score for each candidate item. Experiments on three public datasets Yoochoose1/64, Yoochoose1/4, and RetailRocket show that SR-GTM achieves optimal performance compared with other state-of-the-art methods. More specifically, SR-GTM has improvements of 0.77%, 0.38%, and 3.63% over the best baseline method in P@20 and has improvements of 2.91%, 2.52%, and 2.49% in MRR@20, respectively. [ABSTRACT FROM AUTHOR]

With the evolution of the 5th generation mobile network (5G), the telecommunications industry has considerably affected livelihoods and resulted in the development of national economies worldwide. To increase revenue per customer and secure long-term contracts with users, telecommunications firms and enterprises have launched diverse types of telecommunication packages to satisfy varied user requirements. Several systems for recommending telecommunication packages have been recently proposed. However, extracting effective feature information from large and complex consumption data remains challenging. Conventional methods for the recommendation of telecommunications packages either rely on complex expert feature engineering or fail to perform end-to-end deep learning (DL) during training. In this study, we propose a recommender system based on the Deep and Cross Network (DCN), deep belief network (DBN), embedding, and Word2Vec using the learning abilities of DL-based approaches. The proposed system fits the recommender system for telecommunication packages in terms of click-through rate prediction to provide a potential solution to the recommendation challenges faced by telecommunication enterprises. The proposed model captures the finite order interactional and deep hidden features. Additionally, the text information in the data is used to improve the model's recommendation capability. The proposed method also does not require feature engineering. We conducted comprehensive experiments using real-world datasets, the results of which demonstrated that our proposed method outperformed other methods based on DBNs, DCNs, deep factorization machines, and deep neural networks in terms of the area under the ROC curve, cross entropy (log loss), and recall metrics. [ABSTRACT FROM AUTHOR]

Due to the complexity and uncertainty of customer demand behavior, it was often difficult to obtain satisfactory recommendation results by using the existing online commodity recommendation systems. Therefore, a network commodity intelligent recommendation model based on feature selection and deep belief network was proposed. Based on the basic structure and function of the existing recommendation systems, this paper expounded the interaction process between customers, e-commerce platforms, enterprises, and the recommendation system. By analyzing the internal relationship between customer demand and commodity recommendation, the relationship model between customer demand and commodity recommendation was established. After analyzing the characteristics of customers' demand for goods, a data mining method was used to classify the characteristics of customers' demand behavior, and a feature selection method based on deep belief network (DBN) was proposed to obtain the main information conducive to commodity recommendation. Finally, an e-commerce commodity recommendation algorithm based on feature selection and deep belief network was proposed. The experimental results showed that the network commodity recommendation model proposed in this paper can not only provide customers with satisfactory recommendation results but also has better performance than other traditional recommendation models. The recommendation model proposed in this paper can support different e-commerce website recommendation systems. [ABSTRACT FROM AUTHOR]

The collaborative filtering algorithm widely used in recommendation systems has problems with the sparsity of scoring data and the cold start of new products. A personalized product recommendation model for automated question-answering robots using deep learning is proposed. First, a personalized attention mechanism at the word level and the comment level is proposed, and the comments and users are individually coded. Then, the bidirectional gated recurrent unit (Bi-GRU) is used to construct the score prediction matrix, and through the dynamic collaborative filtering algorithm to integrate the time characteristics of the user's interest changes. Finally, the feature codes of the users and products are input into the Bi-GRU model for learning, so as to output the recommendation list of personalized products of the automated question answering robot. Experimental results based on the JD and Tianchi datasets show that the training loss of the proposed model is lower than 45 and 23, respectively. And HR@15 and MRR@15 exceed 48 and 15, respectively, which are better than other comparison models. It can better adapt to the actual needs of automatic question-answering robots. [ABSTRACT FROM AUTHOR]

Since the increasing development of information technology, its integration and curriculum resources have become a new point of view in education and teaching reform. As a result, educational concepts, teaching models, and learning methods have been changed. Moreover, new requirements for the teaching reform of ideological and political courses in colleges and universities have also been put forward. Aiming at the current educational mechanism of ideological education courses in colleges and universities, which has a single form, is not well targeted and lacks synergy. This paper studies and establishes a brand-new three-dimensional hybrid education concept. The characteristics of this concept include (1) timeliness; that is, the basic views of Marxism are unified with the characteristics of the times; (2) pertinence, that is, the combination of ideological education and the characteristics of the students' growth stage; (3) openness; that is, the new curriculum content and creative thinking are connected. Therefore, the three-dimensional hybrid teaching that combines networked teaching with the traditional teacher-centered teaching model has become an inevitable trend of classroom teaching reform at this stage. This paper develops a college ideological education course recommendation system based on deep learning, based on a hybrid collaborative filtering algorithm, and by introducing the effectiveness of the gradually forgetting curve based on changes in user feature, it better solves the shortcomings of traditional collaborative filtering algorithms, such as low efficiency and weak adaptability. Further, a corresponding recommendation system for ideological guidance courses in colleges and universities has been developed. The system runs stably and has strong practicability and robustness. It is of positive significance for creating an ideological and educational atmosphere with different forms and innovation for teachers and students. [ABSTRACT FROM AUTHOR]

In order to improve the effect of cross-border e-commerce intelligent information recommendation, this paper applies deep learning to the intelligent information processing and intelligent recommendation of e-commerce and proposes an improved version of the topic model to solve the problem of feature extraction of the text of the recommendation system. In order to deal with translation problems, this paper proposes an end-to-end sequence-to-sequence learning method. In addition, this study uses the long tail theory to excavate the mass commodities in the niche and recommends these products to users as suggestions. Finally, this paper proposes a niche product recommendation algorithm based on the graph search strategy based on the graph model. The experiment shows that the cross-border e-commerce intelligent information recommendation system based on deep learning proposed in this paper has a good recommendation effect and meets the recommendation needs of cross-border e-commerce. [ABSTRACT FROM AUTHOR]

The increase of multimedia content in e-commerce and entertainment services creates a new research gap in the field of recommendation systems. The main emphasis of the presented work is on increasing the accuracy of multimedia recommendations using visual semantic content. Recent approaches have shown that the inclusion of visual information is helpful to understand the semantic features for a recommendation model. The researchers have contributed to the field of multimedia item recommendations using low-level visual semantic features. Here, we seek to extend this contribution by exploring the high-level visual semantic content using constant visual attributes for video game recommendation systems. With the exponential growth of multimedia content in the video game industry in the last decade, researchers investigate the importance of personalized video game recommendation techniques. Previous methods have not investigated the importance of visual semantic content for video game recommendations. A practical recommendation system for video games is challenging due to the data diversity, level of user interest, and semantic complexity of features involved. This study proposed a novel method named Deep Visual Semantic Multimedia Recommendation Systems (D_VSMR) to deal with high-level visual features for multimedia recommendation systems. A visual semantic-based video game recommendation system utilizing deep learning methods for visual content learning and user profile learning is introduced. The proposed approach employs content-based techniques to expand users' profiles. The user profile expansion is based on the visual content of games. The required datasets have been obtained from video game e-commerce platforms like Google Play Store and Amazon for evaluation purposes. The evaluation results have shown that the proposed approach's accuracy and effectiveness have been improved up to 95.87% compared to the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and network technology and the widespread popularity of electronic equipment, communication among people is more dependent on the Internet. The Internet has brought great convenience to people's lives and work, and the Internet data is constantly being recorded. People's data information and behavior information, which provides the basis for data mining and recommendation systems, mining users' information and behaviors, and providing "user portraits" for each user, can provide better services to users and it is also an important part of the recommendation system. In one step, this article takes MOOC education resources as the research goal. In order to improve the effective management of MOOC platform resources based on traditional methods, this article combines relevant data sets and recommendation techniques to initially build a learning platform, implements a deep neural network algorithm, and recommends related services. The request and response data were explained, and through the online learning data set, based on the learner's historical learning records, the learning resources were simulated and recommended to the learners. The resource customization module was elaborated. Through the results of resource recommendation, a personalized learning resource recommendation platform was initially realized, which more intuitively demonstrated the recommendation effect and better realized the teaching management of the MOOC platform. [ABSTRACT FROM AUTHOR]

With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second, it becomes more and more difficult to quickly obtain the learning resources you want. Therefore, it is very important to accurately and quickly recommend learning resources to learners. During the last two decades, a large number of different types of recommendation systems were adopted that present the users with contents of their choice, such as videos, products, and educational content recommendation systems. The knowledge graph has been fully applied in this process. The application of deep learning in the recommendation systems has further enhanced their performance. This article proposes a learning resource accurate recommendation model based on the knowledge graph under deep learning. We build a recommendation system based on deep learning that is comprised of a learner knowledge representation (KR) model and a learning resource KR model. Information such as learner's basic information, learning resource information, and other data is used by the recommendation engine to calculate the target learner's score based on the learner KR and the learning resource KR and generate a recommendation list for the target learner. We use mean absolute error (MAE) as the evaluation indicator. The experimental results show that the proposed recommendation system achieves better results as compared to the traditional systems. [ABSTRACT FROM AUTHOR]

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets. [ABSTRACT FROM AUTHOR]

Swimming is not only an entertaining hobby but also a sporting event. It is a sport for strengthening the body. Although there are many swimming coaches, there are different swimming teaching courses. However, choosing the right swimming instructor or course is the motivation for learning swimming activities. To this end, this paper conducts related research on the personalized recommendation system for swimming teaching based on deep learning with the purpose of improving the accuracy of the recommendation system to meet the needs of the users and promote the development of swimming events. This article mainly uses the experimental test method, the system construction method, and the questionnaire survey method to analyze and study the personalized swimming teaching system and the students' attitude to it and draw a conclusion finally. The data results show that the accuracy of the system designed in this paper can meet the basic requirements. Hence, it can bring an excellent experience to the users. According to the questionnaire data, 85%–95% of people have great confidence in the personalized recommendation system. [ABSTRACT FROM AUTHOR]

The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid's real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value. [ABSTRACT FROM AUTHOR]

Recommender systems represent a critical field of AI technology applications. The core function of a recommender system is to recommend items of interest to users, but if it is only user history-based (purchasing or browsing data), it can only recommend similar products to a user, which makes the user feel fatigued (creating so-called "Information Cocoons"). Besides, transaction data (purchasing or browsing data) in various fields usually follow Pareto distributions. Accordingly, 20% of products are purchased or viewed a greater number of times (short-head items), while the remaining 80% of products are purchased or viewed less frequently (long-tail items). Using the traditional recommendation method, considering only the accuracy of recommendations, the coverage rate is relatively low, and most of the recommended items are short-head items. The long-tail item recommendation method not only considers the recommendation of short-head items but also considers recommending more long-tail items to users, thus improving the coverage and diversity of the recommendation results. Long-tail item recommendation research has become a frontier issue in recommendation systems in recent years. While the current research paper is still scarce, there have been related research achievements in top-level conferences in the field of computers, such as VLDB and IJCAI. Due to the fact that there is no review literature in this field, to allow readers to better understand the research status of the long-tail item recommendation method, this paper summarizes the progress of the research on long-tail item recommendation methods (from clustering-based, which began in 2008, to deep learning-based methods, which began in 2020) and the future directions associated with this research. [ABSTRACT FROM AUTHOR]

The recommendation system is an active, personalized, and real-time technology platform proposed in the 1990s to solve the problem of information overload. The recommendation system can constantly adjust the recommendation results according to the real-time behaviors of users. In other words, if the user's interest changes, the recommendation system can present different information to the user. Therefore, the recommendation system is the best way to solve the problem of information overload in entrepreneurial projects. Based on the ConvMF algorithm, this paper proposes an entrepreneurial project recommendation algorithm based on a deep neural network and matrix decomposition. A deep neural network was established for the extraction of the hidden features of entrepreneurial projects, and a convolution neural network was used to process the text description information of entrepreneurial projects. One-hot coding was used to process the regional characteristics and financing round characteristics of entrepreneurial projects, and word embedding was used to process the label features of entrepreneurial projects. The implicit features of users are extracted from the user's rating matrix using matrix decomposition technology. Finally, recommendations are made according to the implicit characteristics of users and the items learned. [ABSTRACT FROM AUTHOR]

Object detection-based deep learning by using the looking and thinking twice mechanism plays an important role in electrical construction work. Nevertheless, the use of this mechanism in object detection produces some problems, such as calculation pressure caused by multilayer convolution and redundant features that confuse the network. In this paper, we propose a self-recurrent learning and gap sample feature fusion-based object detection method to solve the aforementioned problems. The network consists of three modules: self-recurrent learning-based feature fusion (SLFF), residual enhancement architecture-based multichannel (REAML), and gap sample-based features fusion (GSFF). SLFF detects objects in the background through an iterative convolutional network. REAML, which serves as an information filtering module, is used to reduce the interference of redundant features in the background. GSFF adds feature augmentation to the network. Simultaneously, our model can effectively improve the operation and production efficiency of electric power companies' personnel and guarantee the safety of lives and properties. [ABSTRACT FROM AUTHOR]

The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system's performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system's improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients. [ABSTRACT FROM AUTHOR]

The world has seen major developments in the field of e-learning and distance learning, especially during the COVID-19 crisis, which revealed the importance of these two types of education and the fruitful benefits they have offered in a group of countries, especially those that have excellent infrastructure. At the Faculty of Sciences Semlalia, Cadi Ayyad University Marrakech, Morocco, we have created a simple electronic platform for remote practical work (RPW), and its results have been good in terms of student interaction and even facilitating the employment of a professor. The objective of this work is to propose a recommendation system based on deep quality-learning networks (DQNs) to recommend and direct students in advance of doing the RPW according to their skills of each mouse or keyboard click per student. We are focusing on this technology because it has strong, tremendous visibility and problem-solving ability that we will demonstrate in the result section. Our platform has enabled us to collect a range of students' and teachers' information and their interactions with the learning content we will rely on as inputs (a large number of images per second for each mouse or keyboard click per student) into our new system for output (doing the RPW). This technique is reflected in an attempt to embody the virtual teacher's image within the platform and then adequately trained with DQN technology to perform the RPW. [ABSTRACT FROM AUTHOR]

Recently, the application of deep reinforcement learning in the recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns, and sparse data issues in the recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This study proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in the recommender system. Our experiment is carried out on the MovieLens dataset. The result shows that the DRL-based recommender system is superior than traditional algorithms in minimum error, and the application of tags have little effect on accuracy when making up for interpretability. In addition, the DRL-based recommender system has excellent performance on user cold start problems. [ABSTRACT FROM AUTHOR]

Deep learning systems have been phenomenally successful in the fields of computer vision, speech recognition, and natural language processing. Recently, researchers have adopted deep learning techniques to tackle collaborative filtering with implicit feedback. However, the existing methods generally profile both users and items directly, while neglecting the similarities between users' and items' neighborhoods. To this end, we propose the neighborhood attentional memory networks (NAMN), a deep learning recommendation model applying two dedicated memory networks to capture users' neighborhood relations and items' neighborhood relations respectively. Specifically, we first design the user neighborhood component and the item neighborhood component based on memory networks and attention mechanisms. Then, by the associative addressing scheme with the user and item memories in the neighborhood components, we capture the complex user-item neighborhood relations. Stacking multiple memory modules together yields deeper architectures exploring higher-order complex user-item neighborhood relations. Finally, the output module jointly exploits the user and item neighborhood information with the user and item memories to obtain the ranking score. Extensive experiments on three real-world datasets demonstrate significant improvements of the proposed NAMN method over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable to increase user trust and improve the acceptance of recommendations. In this study, we focus on a context‐aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior. [ABSTRACT FROM AUTHOR]

Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users' interests by analyzing users' features, items' features, and users' ratings, as well as users' time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation. [ABSTRACT FROM AUTHOR]