Modeling Multi-factor and Multi-faceted Preferences over Sequential Networks for Next Item Recommendation

Attributes of items carry useful information for accurate recommendations. Existing methods which tried to use items’ attributes relied on either 1) feature-level compression which may introduce much noise information of irrelevant attributes, or 2) itemand attributelevel transition modeling which ignored the mutual effects of multi-factor for users’ behaviors. In addition, these methods failed to capture multi-faceted preferences of users, therefore, the prediction for the next behavior may be affected or misled by the irrelevant facets of preferences. To address these problems, we propose a Sequential Network based Recommendation model, named SNR, to extract and utilize users’ multi-factor and multifaceted preferences for next item recommendation. To model users’ multifactor preferences, we organize the itemand attributelevel sequences of users’ behaviors as unified sequential networks, and propose an attentional gated Graph Convolutional Network model to explore the mutual effects of the preference factors contained in sequential networks. To capture users’ multi-faceted preferences, we propose a multi-faceted preference learning model to simulate the decision-making process of users with the Gumbel sotfmax trick. Finally, we fuse the multi-factor and multifaceted preferences in a unified latent space for next item recommendation. Extensive experiments on four real-world data sets show that the proposed model SNR consistently outperforms several state-of-the-art methods.

[1]  Pengfei Wang,et al.  Learning Hierarchical Representation Model for NextBasket Recommendation , 2015, SIGIR.

[2]  Changsheng Xu,et al.  CSAN: Contextual Self-Attention Network for User Sequential Recommendation , 2018, ACM Multimedia.

[3]  Xing Xie,et al.  Session-based Recommendation with Graph Neural Networks , 2018, AAAI.

[4]  Ji-Rong Wen,et al.  An Attribute-aware Neural Attentive Model for Next Basket Recommendation , 2018, SIGIR.

[5]  Xing Zhang,et al.  Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation , 2019, IJCAI.

[6]  Hui Xiong,et al.  Recurrent Convolutional Neural Network for Sequential Recommendation , 2019, WWW.

[7]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[8]  Yongdong Zhang,et al.  LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation , 2020, SIGIR.

[9]  Alexandros Karatzoglou,et al.  Session-based Recommendations with Recurrent Neural Networks , 2015, ICLR.

[10]  Depeng Jin,et al.  Multi-behavior Recommendation with Graph Convolutional Networks , 2020, SIGIR.

[11]  Xing Shi,et al.  Sequential Heterogeneous Attribute Embedding for Item Recommendation , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[12]  Julian J. McAuley,et al.  Self-Attentive Sequential Recommendation , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[13]  Steffen Rendle,et al.  Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.

[14]  Minyi Guo,et al.  Knowledge Graph Convolutional Networks for Recommender Systems , 2019, WWW.

[15]  Yan Zhao,et al.  DMFP: A Dynamic Multi-faceted Fine-Grained Preference Model for Recommendation , 2019, 2019 IEEE International Conference on Data Mining (ICDM).

[16]  Yongfeng Zhang,et al.  Sequential Recommendation with User Memory Networks , 2018, WSDM.

[17]  Lars Schmidt-Thieme,et al.  Factorizing personalized Markov chains for next-basket recommendation , 2010, WWW '10.

[18]  Ke Wang,et al.  Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding , 2018, WSDM.

[19]  Yang Yang,et al.  Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation , 2020, WSDM.

[20]  Xiao-Ming Wu,et al.  Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning , 2018, AAAI.

[21]  Ben Poole,et al.  Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.

[22]  Hongyuan Zha,et al.  Learning binary codes for collaborative filtering , 2012, KDD.

[23]  Jure Leskovec,et al.  Graph Convolutional Neural Networks for Web-Scale Recommender Systems , 2018, KDD.

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

[25]  Deqing Wang,et al.  Feature-level Deeper Self-Attention Network for Sequential Recommendation , 2019, IJCAI.

[26]  Xing Zhang,et al.  Hierarchical Hybrid Feature Model for Top-N Context-Aware Recommendation , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[27]  CSAN , 2018, Proceedings of the 26th ACM international conference on Multimedia.

[28]  Alexandros Karatzoglou,et al.  Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks , 2017, RecSys.

[29]  Dietmar Jannach,et al.  When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation , 2017, RecSys.

[30]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[31]  Ji-Rong Wen,et al.  S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization , 2020, CIKM.

[32]  Ting Liu,et al.  Attention-over-Attention Neural Networks for Reading Comprehension , 2016, ACL.

[33]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[34]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.