The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. Content-based filtering systems adapt their behavior to individual users by learning their preferences from documents that were already deemed relevant. The learning process aims to construct a profile of the user that can be later exploited in selecting/recommending relevant items. User profiles are generally represented using keywords in a specific language. For example, if a user likes movies whose plots are written in Italian, a content-based filtering algorithm will learn a profile for that user which contains Italian words, thus failing in recommending movies whose plots are written in English, although they might be definitely interesting. Moreover, keywords suffer of typical Information Retrieval-related problems such as polysemy and synonymy. In this paper, we propose a language-independent content-based recommender system, called MARS (MultilAnguage Recommender System), that builds cross-language user profiles, by shifting the traditional text representation based on keywords, to a more complex language-independent representation based on word meanings. The proposed strategy relies on a knowledge-based word sense disambiguation technique that exploits MultiWordNet as sense inventory. As a consequence, content-based user profiles become language-independent and can be exploited for recommending items represented in a language different from the one used in the content-based user profile. Experiments conducted in a movie recommendation scenario show the effectiveness of the approach. © 2011 ACM.
Lops, P., Musto, C., Narducci, F., De Gemmis, M., Basile, P., Semeraro, G. (2011). Learning semantic content-based profiles for cross-language recommendations. In Proceedings of the 1st Workshop on Personalised Multilingual Hypertext Retrieval, PMHR 2011 (pp.26-33) [10.1145/2047403.2047409].
Learning semantic content-based profiles for cross-language recommendations
NARDUCCI, FEDELUCIO;
2011
Abstract
The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. Content-based filtering systems adapt their behavior to individual users by learning their preferences from documents that were already deemed relevant. The learning process aims to construct a profile of the user that can be later exploited in selecting/recommending relevant items. User profiles are generally represented using keywords in a specific language. For example, if a user likes movies whose plots are written in Italian, a content-based filtering algorithm will learn a profile for that user which contains Italian words, thus failing in recommending movies whose plots are written in English, although they might be definitely interesting. Moreover, keywords suffer of typical Information Retrieval-related problems such as polysemy and synonymy. In this paper, we propose a language-independent content-based recommender system, called MARS (MultilAnguage Recommender System), that builds cross-language user profiles, by shifting the traditional text representation based on keywords, to a more complex language-independent representation based on word meanings. The proposed strategy relies on a knowledge-based word sense disambiguation technique that exploits MultiWordNet as sense inventory. As a consequence, content-based user profiles become language-independent and can be exploited for recommending items represented in a language different from the one used in the content-based user profile. Experiments conducted in a movie recommendation scenario show the effectiveness of the approach. © 2011 ACM.
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