Search

Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user's attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

E-commerce is essential application in world wide. In everyday live, we cannot escape from e-commerce transaction. E-commerce requires intelligent machine to deliver product information to customer. Intelligent machine popular called recommender system developed by matrix factorization. Rating is representation of customer expression for satisfied product or service. Unfortunately, number of ratings is too sparse due to majority customer lazy to give rating for e-commerce product. Number of sparse rating matrix have impact in matrix factorization in rating prediction. Moreover, extreme sparse rating has impact degrade performance significantly. Many researchers consider to enhance matrix factorization using customer and product information such as customer demographics information, customer testimony, product review, product description, and etc. In this research, we consider to incorporating Stack Denoising Auto Encoder (SDAE), attention mechanism aims to enhance product review document understanding representation and matrix factorization based on Probabilistic Matric Factorization (PMF) to produce rating prediction. According to experiment report, our model superior over previous work based on hybrid model between PMF, Convolutional Neural Network (CNN) and SDAE that popular called PHD in 1%, and superior over hybrid model between PMF, Long Short Term Memory (LSTM) and SDAE that popular called DDL-PMF in 0.9%, and achieved significantly in 8% over traditional PMF based on RMSE evaluation metrics. [ABSTRACT FROM AUTHOR]

The autoencoder network has been proven to be one of the powerful techniques for recommender systems. Currently, the ways of utilizing autoencoder in recommender systems can be divided into two categories: modeling user-item interaction rely solely on autoencoder and integrating autoencoder with other models. Most existing methods based on autoencoder assume that all features of model's input are equally the same contributing to the final prediction, which can be regarded as attention weight vectors; however, this hypothesis is not reliable, especially when exploring users' interaction frequency with different items. Moreover, combining autoencoder with traditional methods, the usual strategy is to leverage a linear kernel of the inner product of user and item vectors to predict user preferences, which will lead to insufficient expression power and hurt the performance of recommendation when facing data sparsity and cold start problems. To tackle the above two problems, we propose a novel hybrid deep learning model for top-n recommendation, called attentive stacked sparse autoencoder (A-SAERec), which can capture attention weights vector of a user for items, and then combined with the neural matrix factorization to improve the performance of recommender model. Extensive experiments on four real-world datasets show that our A-SAERec algorithm has significant improvements over state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the recommendation system can handle the problem of information overload. Due to many problems in traditional recommendation systems, they can' t handle the discovery of hidden information. This paper proposed an adaptive graph convolution attention neural collaborative filtering recommendation model ( ANGCACF) . Firstly, graph convolution neural network obtained the user-item interaction figure and adaptively aggregated the user-item feature information. Secondly, the model added adaptive extended data to solve the data sparsity for the user-item feature information and used the attention mechanism to redistribute the weight to the user-item feature information and the adaptive extended data. Finally, it obtained the final recommendation result by using the algorithm framework of collaborative filtering based on matrix decomposition. Experiments on MovieLens-lM, MovieLens-lOOK and Amazon-book show that the algorithm is superior to the baseline method in five indexes : precision, recall, Mrr, hit and NDCG. [ABSTRACT FROM AUTHOR]

As there is a huge amount of information on the Internet, people have difficulty in sorting through it to find the required information; thus, the information overload problem becomes a significant issue for users and online businesses. To resolve this problem, many researchers and applications have proposed recommender systems, which apply user-based collaborative filtering, meaning it only considers the users' rating history to analyze their preferences. However, users' text data may contain users' preferences or sentiment information, and such information can be used to analyze users' preferences more precisely. This work proposes a method called the aspect-based deep learning rating prediction method (ADLRP), which can extract the aspects, sentiment, and semantic features from users' and items' reviews. Then, the deep learning method is used to generate users' and items' latent factors. According to these three features, the matrix factorization method is applied to make rating predictions for items. The experimental results show that the proposed method performs better than the traditional rating prediction methods and conventional artificial neural networks. The proposed method can precisely and efficiently extract the sentiments and semantics of each aspect from review texts and enhance the prediction performance of rating predictions. [ABSTRACT FROM AUTHOR]

Intelligent recommendation systems(IRSs) need to face the problems of sparse data and cold start. As one of the intelligent recommendation scenarios, article recommendation has rich and various article information, which is a significant difference from other scenarios. However, the existing methods of article recommender do not fully consider the article information or ignore the implicit correlation between users and articles. Therefore, this paper proposes a Graph-Embedding-inspired Article Recommendation Model (GE-ARM): we capture the "User-Article" correlation feature embeddings by constructing a Bipartite Graph with Resource Allocation (RA-BG); considering the different contributions of the "Title, Abstract" and "Tag, Citation", design an Attention-based Dual-AutoEncoder, combining with Collaborative Filtering to fully capture the article feature embeddings; and update the correlation and article feature embeddings with Probabilistic Matrix Factorization to perform the final recommendation task. The experimental results highlight that GE-ARM outperforms the other known methods on four datasets. [ABSTRACT FROM AUTHOR]

Matrix Factorization is a successful approach for generating an effective recommender system. However, most existing matrix factorization methods suffer from the sparsity and cold-start issues of the user-item matrix as the primary information source of recommender systems. Besides, they are not much scalable to apply to large real-world applications. A main idea to overcome the cold-start and sparsity issues is to use additional information sources such as user/item profiles or user reviews on items. In this paper, a novel Attention-based Deep Learning Recommender System, so-called ADLRS, is proposed to employ the information sources in the matrix factorization method framework. The proposed method uses a language model to represent contextual information such that important features are effectively embedded. Moreover, a deep autoencoder reduces the dimensionality of item vectors embedded by the language model. Then, these vectors are used as regularization terms in the matrix factorization framework to form an objective function. Then, an iterative algorithm is designed to solve the objective function and provide a method prediction of unknown rating values. Experimental results show that the proposed method achieves superior performance compared to other state-of-the-art ones in most cases. Moreover, the improvement rate for sparse datasets and cold items proves that the proposed method effectively deals with sparsity, cold start and scalability problems. • An attention-based deep learning recommender system called ADLRS is proposed. • ADLRS incorporates profiles of items with the framework of matrix factorization. • The BERT language model is used to represents item profiles in the form of vectors. • A deep autoencoder is used to extract effective features and reduce the dimensionality of vectors. • An iterative method is used to solve the objective function and predict unknown ratings. [ABSTRACT FROM AUTHOR]

The emergence of the recommendation system has effectively alleviated the information overload problem. However, traditional recommendation systems either ignore the rich attribute information of users and items, such as the user's social-demographic features, the item's content features, etc., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which introduces the attribute information and adopts the element-wise product between the different information domains to learn the cross-features when conducting information fusion. In addition, the attention mechanism is utilized to distinguish the importance of different cross-features on prediction results. In addition, the IFDNAMF adopts the deep neural network to learn the high-order interaction between users and items. Meanwhile, we conduct extensive experiments on two datasets: MovieLens and Book-crossing, and demonstrate the feasibility and effectiveness of the model. [ABSTRACT FROM AUTHOR]