[논문]추천 다양화 방법을 적용한 콜드 아이템 추천 정확도 향상

한정규; 천세진

doi:10.9717/kmms.2022.25.8.1242

추천 다양화 방법을 적용한 콜드 아이템 추천 정확도 향상
Improved Cold Item Recommendation Accuracy by Applying an Recommendation Diversification Method 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.25 no.8, 2022년, pp.1242 - 1250

한정규 (Division of Computer Engineering and AI, Dong-A University) , 천세진 (Division of Computer Engineering and AI, Dong-A University)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (10)

그림 Fig. 1. Average recommendation score w.r.t. item rank.
표 Table 1. Cosine similarity of contents embedding vectors between user preference and recommended items.
그림 Fig. 2. Contents Distribution based Diversification.
그림 Fig. 3. Overview of Diversification.
그림 Fig. 4. Diversification Algorithm.
표 Table 2. Test data statistics.
표 Table 3. Evaluated Configurations.
표 Table 4. Recommendation Performance of the Diversification method.
그림 Fig. 5. R@20 improvement w.r.t. λ.
그림 Fig. 6. KL@20 improvement w.r.t. λ.

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