Fuzzy integration of structure adaptive SOMs for web content mining

Since exponentially growing web contains giga-bytes of web documents, users are faced with difficulty to find an appropriate web site. Using profile, information retrieval system can personalize browsing of the web by recommending suitable web sites. Userʹs evaluation on web content can be used to predict users’ preference on web sites and construct profiles automatically. User profile represents different aspects of userʹs characteristics, thereby we need an ensemble of classifiers that estimate userʹs preference using web content labeled by user as “like” or “dislike.” Fuzzy integral is a combination scheme that uses subjectively defined relevance of classifiers and structure adaptive self-organizing map (SASOM) is a variant of SOM that is useful to pattern recognition and visualization. In this paper, fuzzy integral-based ensemble of SASOMs trained independently is used to estimate user profile and tested on UCI Syskill & Webert data. Experimental results show that the proposed method can perform better than not only previous naive Bayes classifier but also majority voting of SASOMs.