Collaborative Similarity Embedding for Recommender Systems

We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation. In the proposed framework, we differentiate two types of proximity relations: direct proximity and k-th order neighborhood proximity. While learning from the former exploits direct user-item associations observable from the graph, learning from the latter makes use of implicit associations such as user-user similarities and item-item similarities, which can provide valuable information especially when the graph is sparse. Moreover, for improving scalability and flexibility, we propose a sampling technique that is specifically designed to capture the two types of proximity relations. Extensive experiments on eight benchmark datasets show that CSE yields significantly better performance than state-of-the-art recommendation methods.

[1]  Evangelia Christakopoulou,et al.  Local Item-Item Models For Top-N Recommendation , 2016, RecSys.

[2]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

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

[4]  Deborah Estrin,et al.  Collaborative Metric Learning , 2017, WWW.

[5]  David M. Blei,et al.  Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence , 2016, RecSys.

[6]  Ji-Rong Wen,et al.  Learning Distributed Representations for Recommender Systems with a Network Embedding Approach , 2016, AIRS.

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

[8]  Oren Barkan,et al.  ITEM2VEC: Neural item embedding for collaborative filtering , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

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

[10]  Yi-Hsuan Yang,et al.  Vertex-Context Sampling for Weighted Network Embedding , 2017, ArXiv.

[11]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

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

[13]  George Karypis,et al.  SLIM: Sparse Linear Methods for Top-N Recommender Systems , 2011, 2011 IEEE 11th International Conference on Data Mining.

[14]  S. C. Hui,et al.  Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking , 2017, WWW.

[15]  Raphaël Troncy,et al.  entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation , 2017, RecSys.

[16]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[17]  Jason Weston,et al.  Learning to rank recommendations with the k-order statistic loss , 2013, RecSys.

[18]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[19]  Yi-Hsuan Yang,et al.  Query-based Music Recommendations via Preference Embedding , 2016, RecSys.

[20]  Jason Weston,et al.  WSABIE: Scaling Up to Large Vocabulary Image Annotation , 2011, IJCAI.

[21]  Yupeng Gu,et al.  RaRE: Social Rank Regulated Large-scale Network Embedding , 2018, WWW.

[22]  Chih-Jen Lin,et al.  LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems , 2016, J. Mach. Learn. Res..

[23]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[24]  Stephen J. Wright,et al.  Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent , 2011, NIPS.

[25]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[26]  Yunming Ye,et al.  DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.

[27]  Chang Zhou,et al.  Scalable Graph Embedding for Asymmetric Proximity , 2017, AAAI.

[28]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[29]  Maksims Volkovs,et al.  DropoutNet: Addressing Cold Start in Recommender Systems , 2017, NIPS.

[30]  Ming Gao,et al.  BiNE: Bipartite Network Embedding , 2018, SIGIR.

[31]  Steven Skiena,et al.  Walklets: Multiscale Graph Embeddings for Interpretable Network Classification , 2016, ArXiv.

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

[33]  Neil Yorke-Smith,et al.  TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings , 2015, AAAI.

[34]  Omer Levy,et al.  Neural Word Embedding as Implicit Matrix Factorization , 2014, NIPS.

[35]  Xiangliang Zhang,et al.  WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation , 2018, AAAI.