Authors:
LI-Tung Weng
;
Yue Xu
;
Yuefeng Li
and
Richi Nayak
Affiliation:
Faculty of Information Technology, Queensland University of Technology, Australia
Keyword(s):
Recommender System, Taxonomy, Ecommerce, Cold-start Problem.
Related
Ontology
Subjects/Areas/Topics:
B2B, B2C and C2C
;
B2C/B2B Considerations
;
Business and Social Applications
;
Communication and Software Technologies and Architectures
;
e-Business
;
Enterprise Information Systems
;
Internet and Collaborative Computing
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Web Information Agents
;
Web Information Systems and Technologies
Abstract:
Recommender systems have been widely applied in the domain of ecommerce. They have caught much research attention in recent years. They make recommendations to users by exploiting past users’ item preferences, thus eliminating the needs for users to form their queries explicitly. However, recommender systems’ performance can be easily affected when there are no sufficient item preferences data provided by previous users. This problem is commonly referred to as cold-start problem. This paper suggests another information source, item taxonomies, in addition to item preferences for assisting recommendation making. Item taxonomy information has been popularly applied in diverse ecommerce domains for product or content classification, and therefore can be easily obtained and adapted by recommender systems. In this paper, we investigate the implicit relations between users’ item preferences and taxonomic preferences, suggest and verify using information gain that users who share similar it
em preferences may also share similar taxonomic preferences. Under this assumption, a novel recommendation technique is proposed that combines the users’ item preferences and the additional taxonomic preferences together to make better quality recommendations as well as alleviate the cold-start problem. Empirical evaluations to this approach are conducted and the results show that the proposed technique outperforms other existing techniques in both recommendation quality and computation efficiency.
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