Top-N Recommendation Based on Granular Association Rules

  Recommender systems are popular in e-commerce as they provide users with items of interest.Existing top-K approaches mine the K strongest granular association rules for each user,and then recommend respective K types of items to her.Unfortunately,in practice,many users need only a list of N items that they would like.In this paper,we propose confidence-based and significance based approaches exploiting granular association rules to improve the quality of top-N recommendation,especially for new users on new items.We employ the confidence measure and the significance measure respectively to select strong rules.The first approach tends to recommend popular items,while the second tends to recommend special ones to different users.We also consider granule selection,which is a core issue in granular computing.Experimental results on the well-known MovieLens dataset show that: 1) the confidence-based approach is more accurate to recommend items than the significance-based one; 2) the significance-based approach is more special to recommend items than the confidence-based one; 3) the appropriate setting of granules can help obtaining high recommending accuracy and significance.

作者单位: Lab of Granular Computing, Minnan Normal University, Zhangzhou 363000, China School of Computer Science, Southwest Petroleum University, Chengdu 610500, China;Lab of Granular Computing, Minnan Normal University, Zhangzhou 363000, China
母体文献: The 9th International Conference on Rough Sets and Knowledge Technology (RSKT 2014)(第九届粗糙集与知识技术国际会议)论文集
会议名称: The 9th International Conference on Rough Sets and Knowledge Technology (RSKT 2014)(第九届粗糙集与知识技术国际会议)
会议时间: 2014年1月1日
会议地点: 上海
主办单位: 中国人工智能学会,同济大学
语 种: eng
在线出版日期: 2015年8月31日