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Author Semeraro, Giovanni ♦ Lops, Pasquale ♦ De Gemmis, Marco ♦ Musto, Cataldo ♦ Narducci, Fedelucio
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Content-based recommender systems ♦ Folksonomies ♦ User profiling ♦ Word sense disambiguation
Abstract Museums have recognized the need for supporting visitors in fulfilling a personalized experience when visiting artwork collections, and they have started to adopt recommender systems as a way to meet this requirement. Content-based recommender systems analyze features of artworks previously rated by a visitor and build a visitor model or profile, in which preferences and interests are stored, based on those features. For example, the profile of a visitor might store the names of his or her favorite painters or painting techniques, extracted from short textual descriptions associated with artworks. The user profile is then matched against the attributes of new items in order to provide personalized suggestions. The Web 2.0 (r)evolution has changed the game for personalization from “elitist” Web 1.0, written by few and read by many, to Web content potentially generated by everyone (user-generated content - UGC). One of the forms of UGC that has drawn most attention from the research community is $\textit{folksonomy},$ a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags. In this work, we investigate the problem of deciding whether folksonomies might be a valuable source of information about user interests in the context of recommending digital artworks. We present FIRSt $(\textit{F}olksonomy-based$ $\textit{I}tem$ $\textit{R}ecommender$ $sy\textit{St}em),$ a content-based recommender system which integrates UGC through social tagging in a classic content-based model, letting users express their preferences for items by entering a numerical rating as well as by annotating items with free tags. Experiments show that the accuracy of recommendations increases when tags are exploited in the recommendation process to enrich user profiles, provided that tags are not used as a surrogate for the item descriptions, but in conjunction with them. FIRSt has been developed within the CHAT project “Cultural Heritage fruition & e-learning applications of new Advanced (multimodal) Technologies””, and it is the core of a bouquet of Web services designed for personalized museum tours.
ISSN 15564673
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-10-01
Publisher Place New York
e-ISSN 15564711
Journal Journal on Computing and Cultural Heritage (JOCCH)
Volume Number 5
Issue Number 3
Page Count 22
Starting Page 1
Ending Page 22

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Source: ACM Digital Library