User preference modeling from positive contents for personalized recommendation

With the spread of the Web, users can obtain a wide variety of information, and also can access novel content in real time. In this environment, finding useful information from a huge amount of available content becomes a time consuming process. In this paper, we focus on user modeling for personalization to recommend content relevant to user interests. Techniques used for association rules in deriving user profiles are exploited for discovering useful and meaningful patterns of users. Each user preference is presented the frequent term patterns, collectively called PTP (Personalized Term Pattern) and the preference terms, called PT (Personalized Term). In addition, a content-based filtering approach is employed to recommend content corresponding with user preferences. In order to evaluate the performance of the proposed method, we compare experimental results with those of a probabilistic learning model and vector space model. The experimental evaluation on NSF research award datasets demonstrates that the proposed method brings significant advantages in terms of improving the recommendation quality in comparison with the other methods.