| Summary: | 碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 98 === Collaborative Filtering (CF) recommendation technique is frequently used for building Recommendation Systems (RS). This technique exists for many applications such as recommendations for movies, books, music, and products. The reason of applying CF for recommendations is to find users with similar preferences such that the group of users can be utilized for prediction and the active user’s unrated item is, then, predicted.
To find similar users, the similarity measure must be employed. In the past, many similarity measures are proposed such as Pearson’s Correlation (PCC), Euclidean Distance (ED), Cosine Similarity (COS), or Constrained Pearson’s Correlation (CPC). These methods compute the correlation, distance, or direction for each pair of user’s information. If the number of user is large, the computation will take time. Moreover, as we all know, in e-commerce environment, user information and the amount of users will naturally increase with time. Therefore, a good on-line CF process becomes difficult if the user database grows rapidly.
To improve the performance of CF recommendations, this research proposes the Preference Based Similarity Measure (PBSM) for CF recommendation. The PBSM can distinguish a user’s preferences based on user ratings and can identify the conformity between users’ preferences. This is done by changing user ratings into binary values to represent user’s positive preferences. The proposed method uses the exclusive-NOR (XNOR), the logical operation, to compare the conformity between users’ preferences. Users that have high conformity with the active user are determined to be similar users. The preference of these similar users will be applied for recommendation.
To prove the proposed PBSM can generate better performance, the experiments are designed to allow comparison with traditional similarity measures for CF recommendation. Our experimental results indicate that the proposed PBSM has better prediction outcomes in terms of MAE measure. Also, the PBSM achieves a successful rate which is higher than that of traditional similarity measures. The study proves that the proposed PBSM can enhance the performance of recommendations, and is moreover simple to calculate, which in turn allows for more efficiency than traditional methods.
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