Recommendation algorithms for implicit information

Collaborative filtering (CF) methods are popular for recommender systems. In this paper we focus on exploring how to use implicit and hybrid information to produce efficient recommendations. We suggest a new similarity measure and rating strategy for neighborhood models, and extend original matrix factorization (MF) models to explore implicit information more efficiently. By the mean time, We extend the new MF models to integrate user or item features and obtain a new hybrid model and a corresponding algorithm. Finally we compare our new models with some well known models in our experiments.

[1]  Amir Albadvi,et al.  A hybrid recommendation technique based on product category attributes , 2009, Expert Syst. Appl..

[2]  Yew Jin Lim.  Variational Bayesian Approach to Movie Rating Prediction , 2007 .

[3]  Yehuda Koren,et al.  Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[4]  Yehuda Koren,et al.  Modeling relationships at multiple scales to improve accuracy of large recommender systems , 2007, KDD '07.

[5]  Arkadiusz Paterek,et al.  Improving regularized singular value decomposition for collaborative filtering , 2007 .

[6]  John A. Nelder,et al.  Nelder-Mead algorithm , 2009, Scholarpedia.

[7]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.

[8]  Douglas W. Oard,et al.  Implicit Feedback for Recommender Systems , 1998 .

[9]  Nadine Alameh,et al.  Chaining Geographic Information Web Services , 2003, IEEE Internet Comput..

[10]  Domonkos Tikk,et al.  Investigation of Various Matrix Factorization Methods for Large Recommender Systems , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[11]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[12]  Jinlong Wu.  Binomial Matrix Factorization for Discrete Collaborative Filtering , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[13]  Domonkos Tikk,et al.  Investigation of Various Matrix Factorization Methods for Large Recommender Systems , 2008, ICDM Workshops.

[14]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[15]  Yoon Ho Cho,et al.  Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce , 2004, Expert Syst. Appl..

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

[17]  James Bennett,et al.  The Netflix Prize , 2007 .

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