MARS: Memory Attention-Aware Recommender System

In this paper, we study the problem of modeling users' diverse interests. Previous methods usually learn a fixed user representation, which has a limited ability to represent distinct interests of a user. In order to model users' various interests, we propose a Memory Attention-aware Recommender System (MARS). MARS utilizes a memory component and a novel attentional mechanism to learn deep adaptive user representations. Trained in an end-to-end fashion, MARS adaptively summarizes users' interests. In the experiments, MARS outperforms seven state-of-the-art methods on three real-world datasets in terms of recall and mean average precision. We also demonstrate that MARS has a great interpretability to explain its recommendation results, which is important in many recommendation scenarios.

[1]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

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

[3]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

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

[5]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[6]  Jing Huang,et al.  Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction , 2017, RecSys.

[7]  David A. McAllester,et al.  Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence , 2009, UAI 2009.

[8]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[9]  Tat-Seng Chua,et al.  Neural Factorization Machines for Sparse Predictive Analytics , 2017, SIGIR.

[10]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

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

[12]  Julian J. McAuley,et al.  Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering , 2016, WWW.

[13]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

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

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[16]  Yiqun Liu,et al.  Neural Attentional Rating Regression with Review-level Explanations , 2018, WWW.

[17]  Guorui Zhou,et al.  Deep Interest Network for Click-Through Rate Prediction , 2017, KDD.

[18]  Geoffrey J. Gordon,et al.  Relational learning via collective matrix factorization , 2008, KDD.

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

[20]  Reza Rafeh,et al.  Recommender Systems in ECommerce , 2017 .

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

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

[23]  Xu Chen,et al.  Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources , 2017, CIKM.

[24]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

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

[26]  Hanning Zhou,et al.  A Neural Autoregressive Approach to Collaborative Filtering , 2016, ICML.

[27]  Peng Zhang,et al.  IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models , 2017, SIGIR.

[28]  Benjamin Schrauwen,et al.  Deep content-based music recommendation , 2013, NIPS.

[29]  Huan Liu,et al.  What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation , 2017, WWW.

[30]  Dit-Yan Yeung,et al.  Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks , 2016, NIPS.

[31]  Steffen Rendle,et al.  Factorization Machines with libFM , 2012, TIST.

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

[33]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[34]  Philip S. Yu,et al.  Learning from Multi-View Multi-Way Data via Structural Factorization Machines , 2017, WWW.

[35]  Chen Liu,et al.  Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture , 2017, RecSys.

[36]  A. M. Madni,et al.  Recommender systems in e-commerce , 2014, 2014 World Automation Congress (WAC).

[37]  Tat-Seng Chua,et al.  Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks , 2017, IJCAI.

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

[39]  Xiangnan He,et al.  Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention , 2017, SIGIR.

[40]  Lantao Yu,et al.  Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration , 2017, KDD.

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

[42]  Andrew McCallum,et al.  Ask the GRU: Multi-task Learning for Deep Text Recommendations , 2016, RecSys.

[43]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[44]  Piji Li,et al.  Neural Rating Regression with Abstractive Tips Generation for Recommendation , 2017, SIGIR.

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

[46]  Paul Covington,et al.  Deep Neural Networks for YouTube Recommendations , 2016, RecSys.

[47]  Gao Cong,et al.  Attention-based Group Recommendation , 2018, ArXiv.

[48]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.