Discrete Ranking-based Matrix Factorization with Self-Paced Learning

The efficiency of top-k recommendation is vital to large-scale recommender systems. Hashing is not only an efficient alternative but also complementary to distributed computing, and also a practical and effective option in a computing environment with limited resources. Hashing techniques improve the efficiency of online recommendation by representing users and items by binary codes. However, objective functions of existing methods are not consistent with ultimate goals of recommender systems, and are often optimized via discrete coordinate descent, easily getting stuck in a local optimum. To this end, we propose a Discrete Ranking-based Matrix Factorization (DRMF) algorithm based on each user's pairwise preferences, and formulate it into binary quadratic programming problems to learn binary codes. Due to non-convexity and binary constraints, we further propose self-paced learning for improving the optimization, to include pairwise preferences gradually from easy to complex. We finally evaluate the proposed algorithm on three public real-world datasets, and show that the proposed algorithm outperforms the state-of-the-art hashing-based recommendation algorithms, and even achieves comparable performance to matrix factorization methods.

[1]  Daphne Koller,et al.  Learning specific-class segmentation from diverse data , 2011, 2011 International Conference on Computer Vision.

[2]  Nicolas Kourtellis,et al.  Dynamic Matrix Factorization with Priors on Unknown Values , 2015, KDD.

[3]  Luo Si,et al.  Preference preserving hashing for efficient recommendation , 2014, SIGIR.

[4]  Alexander J. Smola,et al.  Collaborative Filtering on a Budget , 2010, AISTATS.

[5]  Wei Liu,et al.  Learning to Hash for Indexing Big Data—A Survey , 2015, Proceedings of the IEEE.

[6]  Hongyuan Zha,et al.  Learning binary codes for collaborative filtering , 2012, KDD.

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

[8]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[9]  Ivor W. Tsang,et al.  Partial Hash Update via Hamming Subspace Learning , 2017, IEEE Transactions on Image Processing.

[10]  Masatoshi Yoshikawa,et al.  Adaptive web search based on user profile constructed without any effort from users , 2004, WWW '04.

[11]  Xianglong Liu,et al.  Collaborative Hashing , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Dacheng Tao,et al.  Multi-view Self-Paced Learning for Clustering , 2015, IJCAI.

[13]  Shih-Fu Chang,et al.  Semi-Supervised Hashing for Large-Scale Search , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Robert J. Vanderbei,et al.  An Interior-Point Method for Semidefinite Programming , 1996, SIAM J. Optim..

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

[16]  Alan Hanjalic,et al.  List-wise learning to rank with matrix factorization for collaborative filtering , 2010, RecSys '10.

[17]  Xing Xie,et al.  Discrete Content-aware Matrix Factorization , 2017, KDD.

[18]  Xiangnan He,et al.  A Generic Coordinate Descent Framework for Learning from Implicit Feedback , 2016, WWW.

[19]  Lancelot F. James,et al.  Gibbs Sampling Methods for Stick-Breaking Priors , 2001 .

[20]  Zhi-Quan Luo,et al.  Semidefinite Relaxation of Quadratic Optimization Problems , 2010, IEEE Signal Processing Magazine.

[21]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[22]  Qi Xie,et al.  Self-Paced Learning for Matrix Factorization , 2015, AAAI.

[23]  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.

[24]  D. Hunter,et al.  A Tutorial on MM Algorithms , 2004 .

[25]  Wei Liu,et al.  Supervised Discrete Hashing , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Alexander J. Smola,et al.  Maximum Margin Matrix Factorization for Collaborative Ranking , 2007 .

[27]  Daphne Koller,et al.  Self-Paced Learning for Latent Variable Models , 2010, NIPS.

[28]  Deyu Meng,et al.  Easy Samples First: Self-paced Reranking for Zero-Example Multimedia Search , 2014, ACM Multimedia.

[29]  Guowu Yang,et al.  Discrete Personalized Ranking for Fast Collaborative Filtering from Implicit Feedback , 2017, AAAI.

[30]  Qingshan Liu,et al.  A Self-Paced Regularization Framework for Multilabel Learning , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Michael I. Jordan,et al.  A Variational Approach to Bayesian Logistic Regression Models and their Extensions , 1997, AISTATS.

[32]  Mong-Li Lee,et al.  Modeling user's receptiveness over time for recommendation , 2013, SIGIR.

[33]  David J. Fleet,et al.  Fast search in Hamming space with multi-index hashing , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[35]  Huanbo Luan,et al.  Discrete Collaborative Filtering , 2016, SIGIR.