Streaming Session-based Recommendation

Session-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. In this work, we study SR in a practical streaming scenario, namely Streaming Session-based Recommendation (SSR), which is a more challenging task due to (1) the uncertainty of user behaviors, and (2) the continuous, large-volume, high-velocity nature of the session data. Recent studies address (1) by exploiting the attention mechanism in Recurrent Neural Network (RNN) to better model the user's current intent, which leads to promising improvements. However, the proposed attention models are based solely on the current session. Moreover, existing studies only perform SR under static offline settings and none of them explore (2). In this work, we target SSR and propose a Streaming Session-based Recommendation Machine (SSRM) to tackle these two challenges. Specifically, to better understand the uncertainty of user behaviors, we propose a Matrix Factorization (MF) based attention model, which improves the commonly used attention mechanism by leveraging the user's historical interactions. To deal with the large-volume and high-velocity challenge, we introduce a reservoir-based streaming model where an active sampling strategy is proposed to improve the efficiency of model updating. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate the superiority of the SSRM method compared to several state-of-the-art methods in terms of MRR and Recall.

[1]  Hugues Bersini,et al.  Long and Short-Term Recommendations with Recurrent Neural Networks , 2017, UMAP.

[2]  M. de Rijke,et al.  RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation , 2018, AAAI.

[3]  Guandong Xu,et al.  Diversifying Personalized Recommendation with User-session Context , 2017, IJCAI.

[4]  Òscar Celma Herrada.  Music recommendation and discovery in the long tail , 2009 .

[5]  Alexandros Labrinidis,et al.  Avoiding class warfare: managing continuous queries with differentiated classes of service , 2015, The VLDB Journal.

[6]  Alexandros Karatzoglou,et al.  Recurrent Neural Networks with Top-k Gains for Session-based Recommendations , 2017, CIKM.

[7]  Zi Huang,et al.  Restricted Boltzmann Machine Based Active Learning for Sparse Recommendation , 2018, DASFAA.

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

[9]  Lina Yao,et al.  Next Item Recommendation with Self-Attention , 2018, ArXiv.

[10]  Lars Schmidt-Thieme,et al.  Online-updating regularized kernel matrix factorization models for large-scale recommender systems , 2008, RecSys '08.

[11]  Jeffrey Scott Vitter,et al.  Random sampling with a reservoir , 1985, TOMS.

[12]  Òscar Celma,et al.  Music recommendation and discovery in the long tail , 2008 .

[13]  Charu C. Aggarwal,et al.  Recommendations For Streaming Data , 2016, CIKM.

[14]  Lina Yao,et al.  Next Item Recommendation with Self-Attentive Metric Learning , 2018 .

[15]  Sethuraman Panchanathan,et al.  Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Dietmar Jannach,et al.  Evaluation of session-based recommendation algorithms , 2018, User Modeling and User-Adapted Interaction.

[18]  Hui Xiong,et al.  Sequential Recommender System based on Hierarchical Attention Networks , 2018, IJCAI.

[19]  Qiao Liu,et al.  STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation , 2018, KDD.

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

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

[22]  Enhong Chen,et al.  Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors , 2018, KDD.

[23]  Yuan Fang,et al.  Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation , 2018, IJCAI.

[24]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[25]  Dietmar Jannach,et al.  Streamingrec: a framework for benchmarking stream-based news recommenders , 2018, RecSys.

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

[27]  Zi Huang,et al.  Neural Memory Streaming Recommender Networks with Adversarial Training , 2018, KDD.

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

[29]  Lars Schmidt-Thieme,et al.  Real-time top-n recommendation in social streams , 2012, RecSys.

[30]  Fei Wu,et al.  HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction , 2018, IJCAI.

[31]  Lei Zhang,et al.  Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Philippe Preux,et al.  Recurrent Neural Networks for Long and Short-Term Sequential Recommendation , 2018, ArXiv.

[33]  Junjie Yao,et al.  TeRec: A Temporal Recommender System Over Tweet Stream , 2013, Proc. VLDB Endow..

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

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

[36]  Zi Huang,et al.  Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation , 2016, ACM Trans. Inf. Syst..

[37]  Zhiting Hu,et al.  Dynamic User Modeling in Social Media Systems , 2015, TOIS.

[38]  Yong Liu,et al.  Improved Recurrent Neural Networks for Session-based Recommendations , 2016, DLRS@RecSys.

[39]  Zi Huang,et al.  Streaming Ranking Based Recommender Systems , 2018, SIGIR.

[40]  Yi Chang,et al.  Streaming Recommender Systems , 2016, WWW.

[41]  Zhaochun Ren,et al.  Neural Attentive Session-based Recommendation , 2017, CIKM.