DOI QR코드

DOI QR Code

Collaborative Filtering Algorithm Based on User-Item Attribute Preference

  • Ji, JiaQi (Department of Information Center, Hebei Normal University for Nationalities) ;
  • Chung, Yeongjee (Department of Computer and Software Engineering, Wonkwang University)
  • 투고 : 2019.06.15
  • 심사 : 2019.06.21
  • 발행 : 2019.06.30

초록

Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user-item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user-item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user-item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user-item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.

키워드

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Fig. 1. User-based CF flowchart.

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Fig. 2. Influence of λ on MAE.

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Fig. 4. Influence of neighbor set size on MAE.

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Fig. 5. Comparison with baselines.

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Fig. 3. Effect of data sparsity on λ.

Table 1. User-rating matrix

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Table 2. Movie genre matrix

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