사용자 및 항목 기반 통합 유사도의 시간 인지 협력 필터링
Time-aware Collaborative Filtering with User- and Item-based Similarity Integration
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초록
인터넷 상의 전자 상거래 시스템의 인기는 나날이 높아지고 있는데, 추천 시스템은 이러한 시스템들의 핵심 기능으로서, 고객들이 선호할만한 상품을 추천함으로써 원하는 상품을 검색하기 위한노력을 크게 경감시킨다. 협력 필터링 기법은 많은 상업용 시스템에서 성공적으로 구현되어온 추천알고리즘이지만 메모리 기반의 구현 방식은 학계에서의 인기와 유용함에도 불구하고 참조 인접 이웃의 부정확성이 존재한다. 본 연구에서는 이러한 문제점을 해결하고자 사용자와 항목 각각의 인접이웃을 통합하여 활용하고, 이들과의 과거 유사성 보다 최근의 유사성을 더욱 가중하여 추천 리스트 결정에 반영하는 새로운 시간 인지 협력 필터링 기법을 제안한다. 실험 평가를 통하여, 기존의여러 방법들보다 제안 방법이 예측 정확도 측면에서 월등한 성능을 보임을 확인하였다.
Abstract
The popularity of e-commerce systems on the Internet is increasing day by day, and the recommendation system, as a core function of these systems, greatly reduces the effort to search for desired products by recommending products that customers may prefer. The collaborative filtering technique is a recommendation algorithm that has been successfully implemented in many commercial systems, but despite its popularity and usefulness in academia, the memory-based implementation has inaccuracies in its reference neighbor. To solve this problem, this study proposes a new time-aware collaborative filtering technique that integrates and utilizes the neighbors of each item and each user, weighting the recent similarity more than the past similarity with them, and reflecting it in the recommendation list decision. Through the experimental evaluation, it was confirmed that the proposed method showed superior performance in terms of prediction accuracy than other existing methods.
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