A selective ensemble learning based two-sided cross-domain collaborative filtering algorithm. Issue 6 (November 2021)

• Record Type: Journal Article
• Authors: Yu, Xu; Peng, Qinglong; Xu, Lingwei; Jiang, Feng; Du, Junwei; Gong, Dunwei
• Is Part Of: Information processing & management. Volume 58:Issue 6(2021)
• Publication Date: 2021-11
• Subjects: Cross-domain collaborative filtering -- Selective ensemble -- Ensemble learning -- Pareto optimal solutions -- Bi-objective optimization problem; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102691
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Recently, various Cross-Domain Collaborative Filtering (CDCF) algorithms are presented to address the sparsity problem, leveraging ratings of auxiliary domains to improve target domain's recommendation performance. Therein, two-sided CDCF algorithms have shown better performance, given the fact that they can extract both user and item information. However, as the auxiliary domains are not all related to the target domain, utilizing information from all the auxiliary domains may not be optimal and would lead to low efficiency. A T wo-S ided CDCF model based on S elective E nsemble learning considering both A ccuracy and E fficiency (TSSEAE) is proposed to balance recommendation accuracy and efficiency. In TSSEAE, user-sided and item-sided auxiliary domains are firstly combined to improve performance of target domain. Then, CDCF problems are converted to ensemble learning problems, with each combination corresponding to a classifier. In this way, the problem of selecting combinations can be converted to that of selecting classifiers, which is a selective ensemble learning problem. Finally, a bi-objective optimization problem is solved to obtain Pareto optimal solutions for the selective ensemble learning problem. The experimental result on Amazon dataset shows the effectiveness of TSSEAE. Highlights: We investigate that utilizing a subset from auxiliary domains can achieve better performance than all. We convert the subset selection problem to a selective ensemble learning problem. We solve the bi-objective optimization problem to balance accuracy and efficiency.