Hongxi Huang
ABSTRACT
This report aims to propose an improvement to the recommendation system based on collaborative filtering with implicit feedback. Because existing weighted matrix factorization weights all unknown ratings equally, this does not make much sense in reality. To enhance the precision and dependability of weighted matrix factorization, this report suggests combining weighted matrix factorization and Wilson lower bound scores to dynamically alter the weights of unknown ratings, which is more accurate than setting equal weights for all unknown ratings. Thus, exposure bias can be minimized more effectively.
- Jannach, D., Lerche, L., Zanker, M. 2018. Recommending based on implicit feedback.In: Brusilovsky, P., He, D. (eds) Social Information Access. Lecture Notes in Computer Science, vol. 10100. doi: 10.1007/978-3-319-90092-6_14Google Scholar
Cross Ref
- He, X.; Zhang, H.; Kan, M.-Y.; and Chua, T.-S. 2016. Fast matrix factorization for online recommendation with implicit feedback. In SIGIR, 549–558.Google Scholar
- Bayer, I.; He, X.; Kanagal, B.; and Rendle, S. 2017. A generic coordinate descent framework for learning from implicit feedback. In International Conference on World Wide Web, 1341–1350.Google Scholar
- Johnson, C. C. 2014. Logistic matrix factorization for implicit feedback data. Advances in Neural Information Processing Systems 27.Google Scholar
- Rendle, S.; Freudenthaler, C.; Gantner, Z.; and Schmidt-Thieme, L. 2009. Bpr: Bayesian personalized ranking from implicit feedback. In conference on uncertainty in artificial intelligence, 452– 461.Google Scholar
- Wu, Y.; DuBois, C.; Zheng, A. X.; and Ester, M. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In International Conference on Web Search and Data Mining, 153– 162.Google Scholar
- He, X.; Liao, L.; Zhang, H.; Nie, L.; Hu, X.; and Chua, T.-S. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web, 173–182.Google Scholar
- Hu, Y.; Koren, Y.; and Volinsky, C. 2008. Collaborative filtering for implicit feedback datasets. In International Conference on Data Mining, 263–272.Google Scholar
- Kim, Y. -D. and Choi, S. 2009. "Weighted nonnegative matrix factorization," 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1541-1544, doi: 10.1109/ICASSP.2009.4959890.Google ScholarDigital Library
- Shao, W., He, L., Yu, P.S. (2015). Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2,1L2,1 Regularization. In: Appice, A., Rodrigues, P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9284. Springer, Cham. https://doi.org/10.1007/978-3-319-23528-8_20Google ScholarDigital Library
- Li, H., Diao, X., Cao, J., and Zheng,Q. 2018. "Collaborative Filtering Recommendation Based on All-Weighted Matrix Factorization and Fast Optimization," in IEEE Access, vol. 6, pp. 25248-25260, 2018, doi: 10.1109/ACCESS.2018.2828401.Google ScholarCross Ref
- Chen, J., Wang, C., Zhou, S., Shi, Q., Chen, J., Feng, Y., & Chen, C. (2020). Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3470-3477. https://doi.org/10.1609/aaai.v34i04.5751Google ScholarCross Ref
- Kumar, A. (2022). Wilson Lower bound Score and Bayesian Approximation for K star scale rating to Rate products. Retrieved 10 October 2022, from https://medium.com/tech-that-works/wilson-lower-bound-score-and-bayesian-approximation-for-k-star-scale-rating-to-rate-products-c67ec6e30060#:∼:text=Lower% 20bound%20of%20Wilson%20score,and%20the%20use%20this%20score.Google Scholar
- F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872Google ScholarDigital Library
Index Terms
- Weighted Matrix Factorization with Wilson Lower Bound Score
Recommendations
Collaborative filtering using non-negative matrix factorisation
Collaborative filtering is a popular strategy in recommender systems area. This approach gathers users' ratings and then predicts what users will rate based on their similarity to other users. However, most of the collaborative filtering methods have ...
Weighted nonnegative matrix factorization
ICASSP '09: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal ProcessingNonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity constraints are imposed on factor matrices in the decomposition. A large ...
Co-manifold Matrix Factorization
ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern RecognitionMatrix factorization plays a fundamental role in collaborative filtering. In collaborative filtering setting, the rating matrix R is very sparse. Thus, infinite number of matrices can fit the observed entries in the rating matrix. Without additional ...
Comments