A hybrid neural network approach to combine textual information and rating information for item recommendation

Collaborative filtering (CF) is a common method used by many recommender systems. Traditional CF algorithms exploit users’ ratings as the sole information source to learn user preferences. However, ratings usually sparse cause a serious impact on the recommendation results. Most existing CF algorithms use ratings and textual information to alleviate the sparsity of data and then utilize matrix factorization to achieve the latent feature interactions for rating prediction. Nevertheless, the following shortcomings remain in these studies: (1) The word orders and surrounding words of the textual information are ignored. (2) The nonlinearity of feature interactions is seldom exploited. Therefore, we propose a novel hybrid neural network to combine textual information and rating (NCTR) information for item recommendation. The proposed NCTR model is built upon a hybrid neural network framework with fine-grained modeling of latent representation and nonlinearity feature interactions for rating prediction. Specifically, convolution neural network is applied to extract effectively contextual features from textual information. Meanwhile, a fusion layer is exploited to combine features, and the multilayer perceptions are used to model the nonlinear interactions between the merged item latent features and user latent features. Experimental results over five real-world datasets show that NCTR significantly outperforms several state-of-the-art recommendation methods. Source codes are available in https://github.com/luojia527/NCTR_master .

[1]  Michael R. Lyu,et al.  Ratings meet reviews, a combined approach to recommend , 2014, RecSys '14.

[2]  Pramit Mazumdar,et al.  Hidden location prediction using check-in patterns in location-based social networks , 2018, Knowledge and Information Systems.

[3]  Jiawei Han,et al.  Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation , 2017, KDD.

[4]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[5]  Yiqun Liu,et al.  Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews , 2016, IJCAI.

[6]  Ye Wang,et al.  Improving Content-based and Hybrid Music Recommendation using Deep Learning , 2014, ACM Multimedia.

[7]  Nick Craswell,et al.  Query Expansion with Locally-Trained Word Embeddings , 2016, ACL.

[8]  Jing Li,et al.  A personalized point-of-interest recommendation model via fusion of geo-social information , 2018, Neurocomputing.

[9]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[10]  Richi Nayak,et al.  Connecting users and items with weighted tags for personalized item recommendations , 2010, HT '10.

[11]  Nicholas Jing Yuan,et al.  Collaborative Knowledge Base Embedding for Recommender Systems , 2016, KDD.

[12]  Jie Zhang,et al.  TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation , 2014, AAAI.

[13]  Deepak Warrier,et al.  Modeling Contextual Changes in User Behaviour in Fashion e-Commerce , 2017, PAKDD.

[14]  Joung Woo Ryu,et al.  Collaborative Filtering for Recommendation Using Neural Networks , 2005, ICCSA.

[15]  Ju Ren,et al.  A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms , 2019, ACM Comput. Surv..

[16]  James She,et al.  Collaborative Variational Autoencoder for Recommender Systems , 2017, KDD.

[17]  Dit-Yan Yeung,et al.  Learning a Deep Compact Image Representation for Visual Tracking , 2013, NIPS.

[18]  Lei Zheng,et al.  Joint Deep Modeling of Users and Items Using Reviews for Recommendation , 2017, WSDM.

[19]  Yulan He,et al.  Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information , 2016, IEEE Transactions on Knowledge and Data Engineering.

[20]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[21]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[22]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[23]  Tat-Seng Chua,et al.  Fast Matrix Factorization for Online Recommendation with Implicit Feedback , 2016, SIGIR.

[24]  Xing Xie,et al.  Content-Based Collaborative Filtering for News Topic Recommendation , 2015, AAAI.

[25]  Yiqun Liu,et al.  Temporal Relational Ranking for Stock Prediction , 2018, ACM Trans. Inf. Syst..

[26]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[27]  Tiejian Luo,et al.  A Recommendation Model Based on Deep Neural Network , 2018, IEEE Access.

[28]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[29]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[30]  Donghyun Kim,et al.  Convolutional Matrix Factorization for Document Context-Aware Recommendation , 2016, RecSys.

[31]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[32]  Liang Chen,et al.  Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback , 2016, PAKDD.

[33]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[34]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[35]  Lei Yu,et al.  A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems , 2017, AAAI.

[36]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

[37]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[38]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[39]  Martin Ester,et al.  Collaborative Denoising Auto-Encoders for Top-N Recommender Systems , 2016, WSDM.

[40]  Eva Zangerle,et al.  Improving Context-Aware Music Recommender Systems: Beyond the Pre-filtering Approach , 2017, ICMR.

[41]  Tat-Seng Chua,et al.  Item Silk Road: Recommending Items from Information Domains to Social Users , 2017, SIGIR.

[42]  Dit-Yan Yeung,et al.  Collaborative Deep Learning for Recommender Systems , 2014, KDD.

[43]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[44]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[45]  Zhiyuan Liu,et al.  A Neural Network Approach to Joint Modeling Social Networks and Mobile Trajectories , 2016, ArXiv.

[46]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[47]  Qian Wang,et al.  A Context-Aware User-Item Representation Learning for Item Recommendation , 2017, ACM Trans. Inf. Syst..

[48]  MengChu Zhou,et al.  An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems , 2014, IEEE Transactions on Industrial Informatics.

[49]  Falk Scholer,et al.  On Crowdsourcing Relevance Magnitudes for Information Retrieval Evaluation , 2017, ACM Trans. Inf. Syst..

[50]  Yuhong Guo,et al.  Domain Adaptation With Neural Embedding Matching , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[51]  Maoguo Gong,et al.  Tag-aware recommender systems based on deep neural networks , 2016, Neurocomputing.

[52]  Hanna M. Wallach,et al.  Topic modeling: beyond bag-of-words , 2006, ICML.

[53]  Fernando Ortega,et al.  Recommending items to group of users using Matrix Factorization based Collaborative Filtering , 2016, Inf. Sci..

[54]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[55]  Alexander J. Smola,et al.  Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) , 2014, KDD.

[56]  Lina Yao,et al.  Deep Learning Based Recommender System , 2017, ACM Comput. Surv..