ABSTRACT
The need for solving weighted ridge regression (WRR) problems arises in a number of collaborative filtering (CF) algorithms. Often, there is not enough time to calculate the exact solution of the WRR problem, or it is not required. The conjugate gradient (CG) method is a state-of-the-art approach for the approximate solution of WRR problems. In this paper, we investigate some applications of the CG method for new and existing implicit feedback CF models. We demonstrate through experiments on the Netflix dataset that CG can be an efficient tool for training implicit feedback CF models.
- K. Ali and W. van Stam. TiVo: making show recommendations using a distributed collaborative filtering architecture. In Proc. of the 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD '04, pages 394--401, New York, NY, USA, 2004. ACM. Google ScholarDigital Library
- L. Baltrunas and X. Amatriain. Towards time-dependant recommendation based on implicit feedback. In Proc. of the RecSys 2009 Workshop on Context-aware Recommender Systems, 2009.Google Scholar
- M.R. Hestenes and E. Stiefel. Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49:409--436, 1952.Google ScholarCross Ref
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proc. of ICDM-08, 8th IEEE Int. Conf. on Data Mining, pages 263--272, Pisa, Italy, 2008. Google ScholarDigital Library
- A. Paterek. Improving regularized singular value decomposition for collaborative filtering. In Proc. of KDD Cup Workshop at SIGKDD-07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pages 39--42, San Jose, California, USA, 2007.Google Scholar
- I. Pilászy, D. Zibriczky, and D. Tikk. Fast ALS-based matrix factorization for explicit and implicit feedback datasets. In Proceedings of the fourth ACM conference on Recommender Systems, RecSys '10, pages 71--78, Barcelona, Spain, 2010. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In Proc. of the 25th Conf. on Uncertainty in Artificial Intelligence, UAI '09, pages 452--461, 2009. Google ScholarDigital Library
- G. Takács, I. Pilászy, B. Németh, and D. Tikk. Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research, 10:623--656, 2009. Google ScholarDigital Library
Index Terms
- Applications of the conjugate gradient method for implicit feedback collaborative filtering
Recommendations
A Similarity Measure for Collaborative Filtering with Implicit Feedback
ICIC '07: Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial IntelligenceCollaborative Filtering(CF) is a widely accepted method of creating recommender systems. CF is based on the similarities among users or items. Measures of similarity including the Pearson Correlation Coefficient and the Cosine Similarity work quite well ...
Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the WebThe most popular Recommender systems (RSs) employ Collaborative Filtering (CF) algorithms where users explicitly rate items. Based on these ratings, a user-item rating matrix is generated and used to select the items to be recommended for a target user. ...
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsUser-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Comments