Enhancing Content Based Filtering Using Web of Data

Abstract

Recommender systems are very useful to help access to relevant information on the web and to customize search. Content based filtering (CBF) is an alternative among others used to design recommender systems by exploiting items’ contents. Basically, they recommend items based on a comparison between the content of items and user profile. Usually, the content of an item is represented as a set of descriptors or terms; typically the words that occur in text documents. The user profile is represented by the same terms and built up by analysing the content of items he used before. However current CBF recommender systems are mostly devoted to deal with textual resources and cannot be used in their current form to handle the variety of data published on the web especially unstructured data. Another challenge for the existing CBF methods is the issue of new user for whom the system cannot draw any inference due to the lack of information about the user. This paper describes an approach to CBF that aims to deal with these problems on which CBF systems perform poorly. The basic feature of the proposed approach is to incorporate linked data cloud into the information filtering process using a semantic space vector model, and FOAF vocabulary, which is used to define a new distance measure between users, based on their FOAF profiles. We report on some experiments and very promising results of the proposed approach.