An Extension for Bounded-SVD - A Matrix Factorization Method with Bound Constraints for Recommender Systems

• Item type: Journal(1)
• 公開日: 2016-03-15
• タイトル: An Extension for Bounded-SVD - A Matrix Factorization Method with Bound Constraints for Recommender Systems
• タイトル / 言語: en
• タイトル: An Extension for Bounded-SVD - A Matrix Factorization Method with Bound Constraints for Recommender Systems
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：学生・若手研究者論文] collaborative filtering, matrix factorization, bound constraints, recommender systems, stochastic gradient descent
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Information Science & Engineering, Ritsumeikan University
• 著者所属: College of Information Science & Engineering, Ritsumeikan University
• 著者所属: College of Information Science & Engineering, Ritsumeikan University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science & Engineering, Ritsumeikan University
• 著者所属(英): en
• 著者所属(英): College of Information Science & Engineering, Ritsumeikan University
• 著者所属(英): en
• 著者所属(英): College of Information Science & Engineering, Ritsumeikan University
• 著者名: Bang, Hai,Le; Kazuki, Mori; Ruck, Thawonmas
• 著者名(英): Bang, Hai Le; Kazuki, Mori; Ruck, Thawonmas
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this paper, we introduce a new extension for bounded-SVD, i.e., a matrix factorization (MF) method with bound constraints for recommender system. In bounded-SVD, the bound constraints are included in the objective function so that not only the estimation errors are minimized but the constraints are also taken into account during the optimization process. Our previous results on major real-world recommender system datasets showed that bounded-SVD outperformed an existing MF method with bound constraints, BMF, and it is also faster and simpler to implement than BMF. However, an issue of bounded-SVD is that it does not take into account the bias effects in given data. In order to overcome this issue, we propose an extension of bounded-SVD: bounded-SVD bias. Bounded-SVD bias takes into account the rating biases of users and items - known to reside in recommender system data. The experiment results show that the bias extension can improve the performance of bounded-SVD in most cases.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.2 (online); DOI http://dx.doi.org/10.2197/ipsjjip.24.314; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this paper, we introduce a new extension for bounded-SVD, i.e., a matrix factorization (MF) method with bound constraints for recommender system. In bounded-SVD, the bound constraints are included in the objective function so that not only the estimation errors are minimized but the constraints are also taken into account during the optimization process. Our previous results on major real-world recommender system datasets showed that bounded-SVD outperformed an existing MF method with bound constraints, BMF, and it is also faster and simpler to implement than BMF. However, an issue of bounded-SVD is that it does not take into account the bias effects in given data. In order to overcome this issue, we propose an extension of bounded-SVD: bounded-SVD bias. Bounded-SVD bias takes into account the rating biases of users and items - known to reside in recommender system data. The experiment results show that the bias extension can improve the performance of bounded-SVD in most cases.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.2 (online); DOI http://dx.doi.org/10.2197/ipsjjip.24.314; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 57, 号 3, 発行日 2016-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764