Lexical Matching-Based Approach for Multilingual Movie Recommendation Systems

Recommendation systems (RecSys) have been developed for personalized users interaction process to deal with overload information. Movie Content-based recommendation approaches try to measure similarity between movie or users based on relevant information. Nowadays the amount of information on the web exists in several languages. The items description on the RecSys may be not only native languages but also multilingualism. Besides, users interact to the system come from many countries in different languages. However, most of these recommendation systems lack mechanisms to support users overcoming the language problem. Thus, in this paper, we propose a lexical matching-based approach to deal with multilingualism in our process and show efficient experiment for multilingual recommendation system in movie domain.