TLRec:Transfer Learning for Cross-Domain Recommendation

In the era of big data, the available information on the Internet has overwhelmed the human processing capabilities in some commercial applications. Recommendation techniques are indispensable to predict user ratings on items in terms of historical data and deal with the information overload. In many applications, the problem of data sparsity usually results in overfitting and fails to give desirable performance. Therefore, many works have started to investigate the techniques of cross-domain recommendation to overcome the challenge. However, it is not trivial. In this paper, we propose a transfer learning algorithm, named TLRec, for cross-domain recommendation, which exploits the overlapped users and items as a bridge to link different domains and implements knowledge transfer. We learn parameters based on the defined empirical prediction error, smoothness and regularization of user and item latent vectors. We also establish a relation between TLRec and vertex vectoring on bipartite graphs. The experimental result illustrates that TLRec has promising performance and outperforms several state-of-the art approaches on a real dataset.

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

[2]  Ronald Chung,et al.  Integrated personal recommender systems , 2007, ICEC.

[3]  Preslav Nakov,et al.  A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines , 2013, ICML.

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

[5]  Qiang Yang,et al.  Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction , 2009, IJCAI.

[6]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[7]  Martha Larson,et al.  Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering , 2011, UMAP'11.

[8]  Qiang Yang,et al.  Transfer Learning in Collaborative Filtering for Sparsity Reduction , 2010, AAAI.

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

[10]  Yi Zhang,et al.  Efficient bayesian hierarchical user modeling for recommendation system , 2007, SIGIR.

[11]  Michael R. Lyu,et al.  A generalized Co-HITS algorithm and its application to bipartite graphs , 2009, KDD.

[12]  H. Robbins,et al.  A Convergence Theorem for Non Negative Almost Supermartingales and Some Applications , 1985 .

[13]  Guandong Xu,et al.  Personalized recommendation via cross-domain triadic factorization , 2013, WWW.

[14]  Iván Cantador,et al.  Cross-domain recommender systems : A survey of the State of the Art , 2012 .

[15]  Tsvi Kuflik,et al.  Cross-Domain Mediation in Collaborative Filtering , 2007, User Modeling.

[16]  Zhen Lin,et al.  Context-Aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems , 2014, AAAI.

[17]  Ming Gao,et al.  BiRank: Towards Ranking on Bipartite Graphs , 2017, IEEE Transactions on Knowledge and Data Engineering.

[18]  Nicholas Jing Yuan,et al.  Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds , 2016, AAAI.

[19]  Jianxun Liu,et al.  Clustering-Based Collaborative Filtering Approach for Mashups Recommendation over Big Data , 2013, 2013 IEEE 16th International Conference on Computational Science and Engineering.

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

[21]  Antonis Loizou,et al.  How to recommend music to film buffs: enabling the provision of recommendations from multiple domains , 2009 .

[22]  Shou-De Lin,et al.  Matching users and items across domains to improve the recommendation quality , 2014, KDD.

[23]  Qiang Yang,et al.  Transfer learning for collaborative filtering via a rating-matrix generative model , 2009, ICML '09.

[24]  Jennifer Widom,et al.  SimRank: a measure of structural-context similarity , 2002, KDD.

[25]  Shunxiang Wu,et al.  Cross-Platform App Recommendation by Jointly Modeling Ratings and Texts , 2017, ACM Trans. Inf. Syst..

[26]  Tao Chen,et al.  TriRank: Review-aware Explainable Recommendation by Modeling Aspects , 2015, CIKM.