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Recommender systems aim to provide users with preferred items to tackle the information overload problem in the Web era. Social relations, item connections, and usergenerated reviews on items contain abundant potential information. By combining matrix factorization with latent Dirichlet allocation, we integrate ratings, reviews, user similarity and item similarity in recommender systems. The experimental...
Recommendation is an important application that is employed on the Web. In this paper, we propose a method for recommending items to a user by extending a probabilistic inference model in information retrieval. We regard the user’s preference as the query, an item as a document, and explicit and implicit factors as index terms. Additional information sources can be added to the probabilistic inference...
In recent years, we have witnessed the development of location-based services where geographical information plays an important role in reflecting user preferences. This paper aims to provide a unified framework for location-aware recommender systems with the consideration of geographical influence using the matrix factorization method. In the framework, we propose three models corresponding to three...
Collaborative Web search improves search quality by users' working in cooperation and is a subset of social search. Current Web browsers and search engines provide limited support for it. However, it is easier for experts, who are familiar with some topics, to fulfill they needs through search engines due to their backgrounds, domain knowledge and so on. A sharing experts' experiences approach should...
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