Attentive Recurrent Social Recommendation

Collaborative filtering(CF) is one of the most popular techniques for building recommender systems. To alleviate the data sparsity issue in CF, social recommendation has emerged by leveraging social influence among users for better recommendation performance. In these systems, users' preferences over time are determined by their temporal dynamic interests as well as the general static interests. In the meantime, the complex interplay between users' internal interests and the social influence from the social network drives the evolution of users' preferences over time. Nevertheless, traditional approaches either neglected the social network structure for temporal recommendation or assumed a static social influence strength for static social recommendation. Thus, the problem of how to leverage social influence to enhance temporal social recommendation performance remains pretty much open. To this end, in this paper, we present an attentive recurrent network based approach for temporal social recommendation. In the proposed approach, we model users' complex dynamic and general static preferences over time by fusing social influence among users with two attention networks. Specifically, in the dynamic preference modeling process, we design a dynamic social aware recurrent neural network to capture users' complex latent interests over time, where a temporal attention network is proposed to learn the temporal social influence over time. In the general static preference modeling process, we characterize each user's static interest by introducing a static social attention network to model the stationary social influence among users. The output of the dynamic preferences and the static preferences are combined together in a unified end-to-end framework for the temporal social recommendation task. Finally, experimental results on two real-world datasets clearly show the superiority of our proposed model compared to the baselines.

[1]  Chao Liu,et al.  Recommender systems with social regularization , 2011, WSDM '11.

[2]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

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

[4]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[5]  Le Wu,et al.  Leveraging tagging for neighborhood-aware probabilistic matrix factorization , 2012, CIKM.

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

[7]  Xi Chen,et al.  Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization , 2010, SDM.

[8]  Nuria Oliver,et al.  Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering , 2010, RecSys '10.

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

[10]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[11]  Yang Guo,et al.  A survey of collaborative filtering based social recommender systems , 2014, Comput. Commun..

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

[13]  Cecilia Mascolo,et al.  Exploiting place features in link prediction on location-based social networks , 2011, KDD.

[14]  S. Eddy.  Hidden Markov models. , 1996, Current opinion in structural biology.

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

[16]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[17]  Azadeh Iranmehr,et al.  Trust Management for Semantic Web , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

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

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

[20]  Fei Wang,et al.  Social contextual recommendation , 2012, CIKM.

[21]  Alessandro Bozzon,et al.  Interacting Attention-gated Recurrent Networks for Recommendation , 2017, CIKM.

[22]  Jimeng Sun,et al.  Temporal recommendation on graphs via long- and short-term preference fusion , 2010, KDD.

[23]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[24]  Huan Liu,et al.  Exploring temporal effects for location recommendation on location-based social networks , 2013, RecSys.

[25]  Alex Beutel,et al.  Recurrent Recommender Networks , 2017, WSDM.

[26]  Steven B. Andrews,et al.  Power, Social Influence, and Sense Making: Effects of Network Centrality and Proximity on Employee Perceptions. , 1993 .

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

[28]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[29]  Junping Du,et al.  Modeling the Evolution of Users’ Preferences and Social Links in Social Networking Services , 2017, IEEE Transactions on Knowledge and Data Engineering.

[30]  Nicholas Jing Yuan,et al.  Relevance Meets Coverage , 2016, ACM Trans. Intell. Syst. Technol..

[31]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[32]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[33]  Fei-Fei Li,et al.  Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[35]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

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

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

[38]  Andrew W. Senior,et al.  Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.

[39]  Huan Liu,et al.  Social recommendation: a review , 2013, Social Network Analysis and Mining.

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

[41]  Tat-Seng Chua,et al.  Cross-Domain Recommendation via Clustering on Multi-Layer Graphs , 2017, SIGIR.

[42]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[43]  Feng Yu,et al.  A Dynamic Recurrent Model for Next Basket Recommendation , 2016, SIGIR.

[44]  Cameron Marlow,et al.  A 61-million-person experiment in social influence and political mobilization , 2012, Nature.

[45]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[46]  Zhongfei Zhang,et al.  Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs , 2015, SDM.

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