期刊
DECISION SUPPORT SYSTEMS
卷 123, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.dss.2019.113073
关键词
Recommender systems; Bipartite network; Trust relationships; Recommendation diversity; Long-tailed products
类别
资金
- National Natural Science Foundation of China [71671121]
Recommender systems have become an integral and critical part of various online businesses to achieve better user experience and drive customer and revenue growth. Recommendation accuracy and diversity are important criteria to evaluate recommender system performance. Many different strategies have been developed in existing literature to balance the trade-offs between accuracy and diversity. However, those methods often focus on a one-size-fit-all trade-off strategy without considering each individual user' specific recommendation situation, which leads to improvements only in individual diversity or aggregate diversity. In addition, the trust relationships among users have not been studied to improve the trade-off strategy aforementioned. In this paper, we propose an adaptive trust-aware recommendation model based on a new trust measurement developed using a user-item bipartite network. We show via experiments on three different datasets that our model can not only balance and adapt accuracy with both individual and aggregate diversities, but also achieve significant improvements on accuracy for cold-start users and long-tailed items.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据