Reducing the Cold-Start Problem in Content Recommendation through Opinion Classification

Like search engines, recommender systems have become a tool that cannot be ignored by websites with a large selection of products, music, news or simply webpages links. The performance of this kind of system depends on a large amount of information. At the same time, the amount of information on the...

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Vydáno v:2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Ročník 1; s. 204 - 207
Hlavní autoři: Poirier, Damien, Fessant, Francoise, Tellier, Isabelle
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2010
Témata:
Blogs
Buildings
Collaborative filtering
Filtering
Motion pictures
Numerical models
Opinion classification
Recommender systems
Reviews
User Generated Content
Web 2.0
ISBN:9781424484829, 1424484820
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Shrnutí:Like search engines, recommender systems have become a tool that cannot be ignored by websites with a large selection of products, music, news or simply webpages links. The performance of this kind of system depends on a large amount of information. At the same time, the amount of information on the Web is continuously growing, especially due to increased User Generated Content since the apparition of Web 2.0. In this paper, we propose a method that exploits blog textual data in order to supply a recommender system. The method we propose has two steps. First, subjective texts are labelled according to their expressed opinion in order to build a user-item-rating matrix. Second, this matrix is used to establish recommendations thanks to a collaborative filtering technique.
ISBN:9781424484829
1424484820
DOI:10.1109/WI-IAT.2010.87