.png){kind=icon}
返回
Using Hierarchical Graph Maps to Explain Collaborative Filtering Recommendations
.png){kind=icon}
OA
AI
DOI：10.1002/int.21646.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The explanation of and justification for recommendation results are important objectives in recommender systems because such explanations and justifications strongly influence the user's trust in the system. Traditional justification methods are based on textual explanations, which can be inadequate for analysis, comprehension, and decision making on the part of the user. In this paper, we present a method that generates tree graphs that contain the following information: the recommended items, the items that have appeared most often in the recommendation process, the relative importance of the items, and the relationships that exist among the items. The trees obtained in the experiments show (1) the greater novelty of user-to-user results, (2) the overspecialization inherent in the item-to-item approach, and (3) the equilibrium obtained by employing hybrid user-to-user/item-to-item collaborative filtering. The proposed method presents the possibility of extending recommendation result justifications to groups of users and facilitates the explanation of large numbers of recommended items.
Keyword:
EXPLANATION
SYSTEMS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
3.1K
被引数:
8.1K
机构
引用论文
Seismic anisotropy indicates organized melt beneath the Mid-Atlantic Ridge aids seafloor spreading.png)
Geology
OAAI
GeologyIF0
OAAI分享收藏分享收藏A new collaborative filtering metric that improves the behavior of recommender systems一种新的协同过滤度量，可改善推荐系统的行为
PREAI
分享收藏Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions走向下一代推荐系统: 对最新技术和可能扩展的调查
PREAI
分享收藏
查看更多内容