콘텐츠 본문
논문 국내 국내전문학술지(KCI급) New Collaborative Filtering Based on Similarity Integration and Temporal Information
- 학술지 구분 국내전문학술지(KCI급)
- 게재년월 2011
- 저자명 Keunho Choi, Gunwoo Kim, Donghee Yoo, and Yongmoo Suh
- 학술지명 지능정보연구
- 발행처명 한국지능정보시스템학회
- 발행국가 국내
- 논문언어 외국어
- 전체저자수 4
논문 초록 (Abstract)
As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so‐called like‐minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF‐based systems, confirming our hypothesis.
