Title
Improved covering-based collaborative filtering for new users' personalized recommendations.
Abstract
User-based collaborative filtering (UBCF) is widely used in recommender systems (RSs) as one of the most successful approaches, but traditional UBCF cannot provide recommendations with satisfactory accuracy and diversity simultaneously. Covering-based collaborative filtering (CBCF) is a useful approach that we have proposed in our previous work, which greatly improves the traditional UBCF and could provide satisfactory recommendations to an active user which often has sufficient rating information. However, different from an active user, a new user in RSs often has special characteristics (e.g., fewer ratings or ratings concentrating on popular items), and the previous CBCF approach cannot provide satisfactory recommendations for a new user. In this paper, aiming to provide personalized recommendations for a new user, through a detailed analysis of the characteristics of new users, we reconstruct a decision class to improve the previous CBCF and utilize the covering reduction algorithm in covering-based rough sets to remove redundant candidate neighbors for a new user. Furthermore, unlike the previous CBCF, our improved CBCF could provide personalized recommendations without needing special additional information. Experimental results suggest that for the sparse datasets that often occur in real RSs, the improved CBCF significantly outperforms those of existing work and can provide personalized recommendations for a new user with satisfactory accuracy and diversity simultaneously.
Year
DOI
Venue
2020
10.1007/s10115-020-01455-2
KNOWLEDGE AND INFORMATION SYSTEMS
Keywords
DocType
Volume
Covering-based rough sets,User-based collaborative filtering,Covering reduction,Personalized recommendations,New user cold-start
Journal
62.0
Issue
ISSN
Citations 
8.0
0219-1377
1
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Zhipeng Zhang161.79
Yasuo Kudo29526.41
Tetsuya Murai318642.10
Yong-Gong Ren492.88