Cross-Language Information Filtering: Word Sense Disambiguation vs. Distributional Models Cataldo Musto, Fedelucio Narducci, Pierpaolo Basile, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro Department of Computer Science, University of Bari “Aldo Moro”, Italy {cataldomusto,narducci,basilepp,lops,degemmis,semeraro}@di.uniba.it http://www.di.uniba.it/ Abstract. The exponential growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. Anyway, since information exists in many languages, users could also consider as relevant documents written in different lan- guages from the one the query is formulated in. In this context, an emerg- ing requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. How could we represent user information needs or user preferences in a language-independent way? In this paper, we compared two content-based techniques able to pro- vide users with cross-language recommendations: the first one relies on a knowledge-based word sense disambiguation technique that uses Multi- WordNet as sense inventory, while the latter is based on a dimensionality reduction technique called Random Indexing and exploits the so-called distributional hypothesis in order to build language-independent user pro- files. Since the experiments conducted in a movie recommendation scenario show the effectiveness of both approaches, we tried also to underline strenghts and weaknesses of each approach in order to identify scenarios in which a specific technique fits better. Keywords: Cross-language Recommender System, Content-based Rec- ommender System, Word Sense Disambiguation, Random Indexing. 1 Introduction Nowadays the amount of information we have to deal with is usually greater than the amount of information we can process in an effective way. For this rea- son, user modeling and personalized information access are becoming essential to propose only (or firstly) the information that appear relevant or someway related to the informative need of the target user.Information Filtering (IF) sys- tems are rapidly emerging in this context since they are helpful for carrying out this task in an effective way. These systems adapt their behavior to individual users by learning their preferences and storing them in a user profile. Filtering R. Pirrone and F. Sorbello (Eds.): AI*IA 2011, LNAI 6934, pp. 250–261, 2011. c  Springer-Verlag Berlin Heidelberg 2011