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.

最近，提出了各种跨域协作过滤 (CDCF) 算法来解决稀疏问题，利用辅助域的评级来提高目标域的推荐性能。其中，双面CDCF算法表现出更好的性能，因为它们可以同时提取用户和项目信息。然而，由于辅助域并不都与目标域相关，因此利用来自所有辅助域的信息可能不是最佳的，并且会导致效率低下。一个牛逼WO-小号ided基于CDCF模型小号的选修Ë nsemble学习同时考虑一个ccuracy和è效率（TSSEAE）被提出来平衡推荐的准确性和效率。在 TSSEAE 中，首先结合用户侧和项目侧辅助域来提高目标域的性能。然后，CDCF 问题被转换为集成学习问题，每个组合对应一个分类器。这样，选择组合的问题就可以转化为选择分类器的问题，这是一个选择性集成学习问题。最后，解决双目标优化问题以获得选择性集成学习问题的帕累托最优解。Amazon 数据集上的实验结果显示了 TSSEAE 的有效性。