| Summary: | 碩士 === 國立交通大學 === 管理學院資訊管理學程 === 101 === The recommendation systems are widely used on the network to help users quickly find suitable or interested products. In this area, many recommendation techniques, such as Content-based Approach, Collaborative Filtering Approach, and Hybrid Approach, have been developed. Although the recommendation technology has become mature, there are still some problems. Recommendation systems cannot provide the correct information if we don’t have enough user information. The precision of the recommendation system will be increased dramatically, if a system gets more user potential information.
In this study, we attempts to propose a recommendation mechanism design combined with Bayesian networks and incentive theory. In lack of user profile information, our recommendation mechanism still has high precision. This mechanism approach uses Bayesian network to generate association rules from user profiles, a source of information used to expand the user profile, and avoid the problem of user profile shortage. In the new items problem, based on the theory of incentives, we propose a mechanism for encouraging data sharing. This encouraging mechanism satisfies individual rationality and incentive compatibility. Our experiments show that our proposed mechanism can significantly improve the performance of a recommender system under the short user profile situation.
|
|---|
