Preference Difference Metric을 이용한 아이템 분류방식의 추천알고리즘

국문 초록 (Abstract)

기존의 협업필터링 기반의 추천시스템에 대한 연구는 정확한 평점예측에 집중되면서 추천시스템의 수행시간이 길어지게 되고, 선호아이템을 짧은 시간에 추천해주는 본래의 목적에서 멀어지게 되었다. 본 논문에서는 Preference Difference Metric을 이용하여 평점예측이 아닌 선호 아이템의 분류를 통한 추천을 수행하여 수행시간을 단축하고 정확도를 유지하는 추천 알고리즘을 제안한다.

다국어 초록 (Multilingual Abstract)

In recent years, research on collaborative filtering-based recommendation systems emphasized the accuracy of rating predictions, and this has led to an increase in computation time. As a result, such systems have divergeded from the original purpose of making quick recommendations. In this paper, we propose a recommendation algorithm that uses a Preference Difference Metric to reduce the computation time and to maintain adequate performance. The system recommends items according to their preference classification.

목차 (Table of Contents)

• 요약
• Abstract
• 1. 서론
• 2. 관련연구
• 3. Preference Difference Metric을 통한 아이템 분류
• 4. 실험 및 결과
• 5. 결론
• References

참고문헌 (Reference)

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2 Y. Koren, "Matrix Factorization Techniques for Recommender Systems" 42 (42): 2009-,

3 B. Sarwar, "Item-based collaborative filtering recommendation algorithm" 285-295, 2001

4 Tsang-Hsiang Cheng, "Collaborative Filtering With User Interest Evolution" 1-12, 2011

5 Hyung Jun Ahn, "A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem" 178 (178): 37-51, 2008

6 J. Bobadilla, "A collaborative filtering approach to mitigate the new user cold start problem" 26 : 225-238, 2012