A selective ensemble learning based two-sided cross-domain collaborative filtering algorithm

摘要

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 Two-Sided CDCF model based on Selective Ensemble learning considering both Accuracy and Efficiency (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.