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

Damien Poirier (1, 2) , Françoise Fessant (1) , Isabelle Tellier (2)

1 Orange Labs [Lannion]
2 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans

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.

Domains: Machine Learning [cs.LG]; Information Retrieval [cs.IR]; Web

Damien Poirier, Françoise Fessant, Isabelle Tellier. Reducing the Cold-Start Problem in Content Recommendation Through Opinion Classification. Web Intelligence, Aug 2010, Toronto, Canada. ⟨inria-00514533⟩