Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Improved Cold Item Recommendation Accuracy by Applying an Recommendation Diversification Method

추천 다양화 방법을 적용한 콜드 아이템 추천 정확도 향상

  • Han, Jungkyu (Division of Computer Engineering and AI, Dong-A University) ;
  • Chun, Sejin (Division of Computer Engineering and AI, Dong-A University)
  • Received : 2022.08.11
  • Accepted : 2022.08.26
  • Published : 2022.08.31
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Abstract

When recommending cold items that do not have user-item interactions to users, even we adopt state-of-the-arts algorithms, the predicted information of cold items tends to have lower accuracy compared to warm items which have enough user-item interactions. The lack of information makes for recommender systems to recommend monotonic items which have a few top popular contents matched to user preferences. As a result, under-diversified items have a negative impact on not only recommendation diversity but also on recommendation accuracy when recommending cold items. To address the problem, we adopt a diversification algorithm which tries to make distributions of accumulated contents embedding of the two items groups, recommended items and the items in the target user's already interacted items, similar. Evaluation on a real world data set CiteULike shows that the proposed method improves not only the diversity but also the accuracy of cold item recommendation.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1G1A1092248) and also supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1050937).

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