Browsing by Author "SUSHMITHA, R"

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    Space Touchy Proposal with Client Thing Sub Bunch Examination
    (2020-09-23T09:59:12Z) SUSHMITHA, R
    LAST decades have witnessed the overwhelming supply of online information with the evolution of the Internet. Thus, recommender systems have been indispensable nowadays, which support users with possibly different judgments and opinions in their quest for information, through taking into account the diversity of preferences and the relativity of information value. Collaborative Filtering (CF) is an effective and widely adopted recommendation approach. Different from content-based recommender systems which rely on the profiles of users and items for predictions, CF approaches make predictions by only utilizing the user-item interaction information such as transaction history or item satisfaction expressed in ratings, etc. As more attention is paid on personal privacy, CF systems become increasingly popular, since they do not require users to explicitly state their personal information limit the performance of typical CF methods. On one hand, user’s interests always center on some specific domains but not all the domains. However, typical CF approaches do not treat these domains distinctively. On the other hand, the fundamental assumption for typical CF approaches is that users rate similarly on partial items, and hence they will rate on all the other items similarly. However, it is observed that this assumption is not always so tenable. Usually, the collaborative effect among users varies across different domains. In other words, two users have similar tastes in one domain cannot infer that they have similar taste in other domain. Taking an intuitive example, two users who love romantic movies probably have totally different preference in action movies. Thus, it is more reasonable and necessary to automatically mine different domains and perform domain sensitive CF for recommender systems.