Cross-Domain Recommendation Algorithm Based on Knowledge Aggregation and Transfer

被引：0
|
作者：
Liu Z. [1 ]
Tian J.-Y. [1 ]
Yuan B.-X. [1 ]
Sun Y.-Q. [1 ]
机构：
[1] School of Computer and Information Technology, Beijing Jiaotong University, Beijing
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 10期
关键词：
Cross-domain recommendation; Knowledge aggregation; Matrix factorization; Transfer learning;
D O I：
10.3969/j.issn.0372-2112.2020.10.008
中图分类号：
学科分类号：
摘要：
Cross-domain recommendation can study the knowledge of the auxiliary domains to enrich the knowledge in the target domain,so as to improve the recommendation precision in the target domain.This paper proposes an aggregation and transfer collaborative filtering algorithm for cross-domain recommendation (ATCF).In order to represent the sharing knowledge in different domains,the knowledge in the auxiliary domain and the target domain are fully aggregated,through two levels of matrix concatenation.Moreover,the personalized knowledge of the target domain is represented by knowledge transferring from auxiliary domain.By fusion the sharing and the personalized knowledge,we can obtain the final rating.Two different cross-domain datasets are used for the experiments.Our efforts show that the ATCF algorithm has better recommendation performance. © 2020, Chinese Institute of Electronics. All right reserved.
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收藏
页码：1928 / 1932
页数：4
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