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Erschienen in:
2018 | OriginalPaper | Buchkapitel
An Effective FP-Tree-Based Movie Recommender System
verfasst von : Sam Quoc Tuan, Nguyen Thi Thanh Sang, Dao Tran Hoang Chau
Erschienen in: Information Systems Design and Intelligent Applications
Verlag: Springer Singapore
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Abstract
Movie recommender systems play an important role in introducing users to the most interesting movies efficiently. It is useful for users to find what they want in a large numerous of various movies on the Web quickly. The performance of movie recommendation is influenced by many factors, such as user behavior, user ratings. Therefore, the aim of this study is to mine datasets of user ratings and user behaviors in order to recommend the most suitable movies to active users. User behaviors are sequences of users’ movie viewing activities which can be discovered by a frequent-pattern tree (FP-Tree). The FP-tree is then modified with rating data and an effective recommendation strategy can improve the recommendation performance of the FP-tree. A MovieLens dataset which is public and popular for evaluating movie recommender systems is observed and examined for assessing the proposed method.