| Summary: | 碩士 === 國立中正大學 === 電機工程研究所 === 103 === User classification in a recommendation system is a very important research direction, because it shortens the process time of the recommendation system. However, some negative information will be accompanied at the same time when the number of users grows. Without proper processing of information, the cold-start problem or the sparsity problem may arise. When the system has a new user, recommendation may be unsuccessful, because the new user does not have his/her own usage. In order to avoid these things to happen in a recommendation system, this research has added user data and status to aid the recommendation system. In this study, we use the goal-oriented way to find an appropriate user characteristics based on user’s status attributes to be include in the recommendation system. The recommendation system will be more flexible by adding status information, because the recommendation system has more reference information. In this study, MovieLens is used as the experimental data. Because data integrity is high and usable reference materials are also more, its recommended accuracy will be higher. By adding the nearest neighbor method to the recommendation system, we obtain a better result. When the same method is applied to a small database the recommendation accuracy deteriorates. However, when adding the user information, we have a positive effect for the recommendation results.
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