TitleTaxonomy Based Personalized News Recommendation: Novelty and Diversity
AuthorsRao, Junyang
Jia, Aixia
Feng, Yansong
Zhao, Dongyan
AffiliationPeking Univ, ICST, Beijing, Peoples R China.
KeywordsPersonalized Recommender System
Novelty and Diversity
Taxonomy
Online Encyclopedia
SIMILARITY
RETRIEVAL
Issue Date2013
CitationWEB INFORMATION SYSTEMS ENGINEERING - WISE 2013, PT I.8180(209-218).
AbstractRecommender systems are designed to help users quickly access large volumes of information according to their profiles. Most previous works in recommender systems have put their emphasis on the accuracy of finding the most similar items according to a user's profile, while often ignoring other aspects that may affect users' experiences in practice, e.g., the novelty and diversity issues within a recommendation list. In this paper, we focus on utilizing taxonomic knowledge extracted from an online encyclopedia to boost a content-based personalized news recommender system without much human involvement. Given a recommendation list, we improve a user's satisfaction by introducing the taxonomy based novelty and diversity metrics to include novel, but potentially related items into the list, and filter out redundant ones. The experimental results show that the coarse grained knowledge resources can help a content-based news recommender system provides accurate as well as user-oriented recommendations.
URIhttp://hdl.handle.net/20.500.11897/405688
ISSN0302-9743
DOI10.1007/978-3-642-41230-1_18
IndexedEI
CPCI-S(ISTP)
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