한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제23권12호
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  • Pages.219-226
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  • 2018
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Development of a Personalized Similarity Measure using Genetic Algorithms for Collaborative Filtering

  • Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
  • 투고 : 2018.10.15
  • 심사 : 2018.11.08
  • 발행 : 2018.12.31
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초록

Collaborative filtering has been most popular approach to recommend items in online recommender systems. However, collaborative filtering is known to suffer from data sparsity problem. As a simple way to overcome this problem in literature, Jaccard index has been adopted to combine with the existing similarity measures. We analyze performance of such combination in various data environments. We also find optimal weights of factors in the combination using a genetic algorithm to formulate a similarity measure. Furthermore, optimal weights are searched for each user independently, in order to reflect each user's different rating behavior. Performance of the resulting personalized similarity measure is examined using two datasets with different data characteristics. It presents overall superiority to previous measures in terms of recommendation and prediction qualities regardless of the characteristics of the data environment.

키워드

CPTSCQ_2018_v23n12_219_f0001.png 이미지

Fig. 1. Workflow for obtaining optimal weights of the proposed similarity measure

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Fig. 2. Description of our genetic algorithm

CPTSCQ_2018_v23n12_219_f0003.png 이미지

Fig. 3. Comparison of MAE with MovieLens dataset

CPTSCQ_2018_v23n12_219_f0004.png 이미지

Fig. 4. Comparison of MAE with Jester dataset

CPTSCQ_2018_v23n12_219_f0005.png 이미지

Fig. 5. Comparison of F1 with MovieLens dataset

CPTSCQ_2018_v23n12_219_f0006.png 이미지

Fig. 6. Comparison of F1 with Jester dataset

Table 1. Parameters for the genetic operation

CPTSCQ_2018_v23n12_219_t0001.png 이미지

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