Matching users and items across domains to improve the recommendation quality

Given two homogeneous rating matrices with some overlapped users/items whose mappings are unknown, this paper aims at answering two questions. First, can we identify the unknown mapping between the users and/or items? Second, can we further utilize the identified mappings to improve the quality of recommendation in either domain? Our solution integrates a latent space matching procedure and a refining process based on the optimization of prediction to identify the matching. Then, we further design a transfer-based method to improve the recommendation performance. Using both synthetic and real data, we have done extensive experiments given different real life scenarios to verify the effectiveness of our models. The code and other materials are available at http://www.csie.ntu.edu.tw/~r00922051/matching/

[1]  Laks V. S. Lakshmanan,et al.  HeteroMF: recommendation in heterogeneous information networks using context dependent factor models , 2013, WWW.

[2]  Fan Zhang,et al.  What's in a name?: an unsupervised approach to link users across communities , 2013, WSDM.

[3]  Nicholas Jing Yuan,et al.  We know how you live: exploring the spectrum of urban lifestyles , 2013, COSN '13.

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

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

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

[7]  Vitaly Shmatikov,et al.  De-anonymizing Social Networks , 2009, 2009 30th IEEE Symposium on Security and Privacy.

[8]  Geoffrey J. Gordon,et al.  Relational learning via collective matrix factorization , 2008, KDD.

[9]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

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

[11]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

[12]  Vitaly Shmatikov,et al.  Robust De-anonymization of Large Sparse Datasets , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

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

[14]  Reza Zafarani,et al.  Connecting users across social media sites: a behavioral-modeling approach , 2013, KDD.

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

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

[17]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[18]  Michael Gamon,et al.  Linguistic correlates of style: authorship classification with deep linguistic analysis features , 2004, COLING.

[19]  Qiang Yang,et al.  Transfer learning in heterogeneous collaborative filtering domains , 2013, Artif. Intell..

[20]  Yehuda Koren,et al.  The Yahoo! Music Dataset and KDD-Cup '11 , 2012, KDD Cup.

[21]  Bin Cao,et al.  Multi-Domain Collaborative Filtering , 2010, UAI.