NDLI: Exploiting Item Taxonomy for Solving Cold-Start Problem in Recommendation Making

Content Provider	IEEE Xplore Digital Library
Author	Li-Tung Weng Yue Xu Yuefeng Li Nayak, R.
Copyright Year	2008
Description	Author affiliation: Fac. of Inf. Technol., Queensland Univ. of Technol., Brisbane, QLD (Li-Tung Weng; Yue Xu; Yuefeng Li; Nayak, R.)
Abstract	Recommender systems' performance can be easily affected when there are no sufficient item preferences data provided by previous users, and it is commonly referred to as cold-start problem. This paper suggests another information source, item taxonomies, in addition to item preference data 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 that users who share similar item 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.
Starting Page	113
Ending Page	120
File Size	587117
Page Count	8
File Format	PDF
ISBN	9780769534404
ISSN	10823409
DOI	10.1109/ICTAI.2008.97
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Taxonomy Recommender systems Collaboration Information filtering Information retrieval Permission Artificial intelligence Information technology Australia Electronic commerce Recommender
Content Type	Text
Resource Type	Article

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