Authors:
Chin-Hui Lai
1
;
Duen-Ren Liu
2
and
Cai-Sin Lin
2
Affiliations:
1
Chung Yuan Christian University, Taiwan
;
2
National Chiao Tung University, Taiwan
Keyword(s):
Collaborative Filtering, Document Recommendation, Group Trust, Role Relationship, Personal Trust, Trust-based Recommender System.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Information Retrieval
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Software Engineering
;
Web Information Systems and Technologies
Abstract:
Collaborative filtering (CF) recommender systems have been used in various application domains to solve the information-overload problem. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. Some researchers have proposed rating-based trust models to derive the trust values based on users’ past ratings of items, or based on explicitly specified relations (e.g. friends) or trust relationships. The rating-based trust model may not be effective in CF recommendations, due to unreliable trust values derived from very few past rating records. In this work, we propose a hybrid personal trust model which adaptively combines the rating-based trust model and explicit trust metric to resolve the drawback caused by insufficient past rating records. Moreover, users with similar preferences usually form a group to share items (knowledge) with each other, and thus users’ preferences may be affected by g
roup members. Accordingly, group trust can enhance personal trust to support recommendation from the group perspective. Eventually, we propose a recommendation method based on a hybrid model of personal and group trust to improve recommendation performance. The experiment result shows that the proposed models can improve the prediction accuracy of other trust-based recommender systems.
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