사용자 기반 협력 필터링을 위한 시간 맥락을 반영한 자카드 계수
Jaccard Index Reflecting Time-Context for User-based Collaborative Filtering
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초록
추천 시스템의 구현 방식들 중 하나인 사용자 기반의 협력 필터링 기법은 유사한 평가 이력을가진 이웃 사용자들의 산출을 기반으로 하여, 이들의 선호 항목들을 추천한다. 그러나 공통된 평가 이력이 적을 경우에 추천의 질이 현저히 저하되는 데이터 희소성 문제를 근본적으로 갖고 있다. 이러한 문제의 해결을 위하여 많은 기존 연구에서 자카드 계수를 유사도 척도와 접목하는 다양한 방법들을 제안해 왔다. 본 연구에서는 자카드 계수에 시간 인지 개념을 도입하여 공통 항목의 평가 시간에 따라 다른 비중으로 가중합하는 방안을 제시한다. 다양한 성능 척도와 시간 주기를 활용하여 실험을 수행한 결과, 제안 방법이 대부분의 척도에서 원래의 자카드 계수에 비해 가장 우수한 성능을 보였으며, 최적의 시간 주기는 성능 척도의 종류에 따라 다름을 확인하였다.
Abstract
The user-based collaborative filtering technique, one of the implementation methods of the recommendation system, recommends the preferred items of neighboring users based on the calculations of neighboring users with similar rating histories. However, it fundamentally has a data scarcity problem in which the quality of recommendations is significantly reduced when there is little common rating history. To solve this problem, many existing studies have proposed various methods of combining Jaccard index with a similarity measure. In this study, we introduce a time-aware concept to Jaccard index and propose a method of weighting common items with different weights depending on the rating time. As a result of conducting experiments using various performance metrics and time intervals, it is confirmed that the proposed method showed the best performance compared to the original Jaccard index at most metrics, and that the optimal time interval differs depending on the type of performance metric.
참고문헌 (27)
- Rui Chen, "A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks", IEEE Access, 6, pp.64301~64320 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Bilin Shao, "A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph", Expert Systems with Applications, 165, pp.113764~ (2021), Elsevier BV[DOI]
- Mahdi Jalili, "Evaluating Collaborative Filtering Recommender Algorithms: A Survey", IEEE Access, 6, pp.74003~74024 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Fethi Fkih, "Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison", Journal of King Saud University - Computer and Information Sciences, 34(9), pp.7645~7669 (2022), Elsevier BV[DOI]
- K. G. Saranya, "Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique", Indian Journal of Science and Technology, 9(29) (2016), Indian Society for Education and Environment[DOI]
- Halime Khojamli, "Survey of similarity functions on neighborhood-based collaborative filtering", Expert Systems with Applications, 185, pp.115482~ (2021), Elsevier BV[DOI]
- Hui-Feng Sun, "JacUOD: A New Similarity Measurement for Collaborative Filtering", Journal of Computer Science and Technology, 27(6), pp.1252~1260 (2012), Springer Science and Business Media LLC[DOI]
- G. Koutrica, "FlexRecs: Expressing and Combining Flexible Recommendations", Proc. the ACM SIGMOD International Conference on Management of Data, pp.745~758 (2009)
- Soojung Lee, "Improving Jaccard Index for Measuring Similarity in Collaborative Filtering", Information Science and Applications 2017, pp.799~806 (2017), Springer Singapore[DOI]
- Pedro G. Campos, "Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols", User Modeling and User-Adapted Interaction, 24(1-2), pp.67~119 (2013), Springer Science and Business Media LLC[DOI]
- Amit Livne, "Evolving context-aware recommender systems with users in mind", Expert Systems with Applications, 189, pp.116042~ (2022), Elsevier BV[DOI]
- Jonathan L. Herlocker, "An Algorithmic Framework for Performing Collaborative Filtering", ACM SIGIR Forum, 51(2), pp.227~234 (2017), Association for Computing Machinery (ACM)
- H. -J. Kwon, "Improving Prediction Accuracy using Entropy Weighting in Collaborative Filtering", Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, pp.40~45 (2009)
- Haifeng Liu, "A new user similarity model to improve the accuracy of collaborative filtering", Knowledge-Based Systems, 56, pp.156~166 (2014), Elsevier BV[DOI]
- Neda Mohammadi, "A two-stage location-sensitive and user preference-aware recommendation system", Expert Systems with Applications, 191, pp.116188~ (2022), Elsevier BV[DOI]
- Y. Huai-Zhen, "An Enhanced Collaborative Filtering Algorithm Based on Time Weight", International Symposium on Information Engineering and Electronic Commerce, pp.262~265 (2009)
- Y. Ding, "Time Weight Collaborative Filtering", Fourteenth ACM International Conference on Information and Knowledge Management, pp.485~492 (2005)
- L. He, "A Time-Context-Based Collaborative Filtering Algorithm", IEEE International Conference on Granular Computing, pp.209~213 (2009)
- Nan Zheng, "A recommender system based on tag and time information for social tagging systems", Expert Systems with Applications, 38(4), pp.4575~4587 (2011), Elsevier BV[DOI]
- Charinya Wangwatcharakul, "A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution", Expert Systems with Applications, 185, pp.115626~ (2021), Elsevier BV[DOI]
- Guangxia Xu, "A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context", Journal of Electrical and Computer Engineering, 2019, pp.7070487~ (2019), Hindawi Limited[DOI]
- Shuai Ding, "Time-aware cloud service recommendation using similarity-enhanced collaborative filtering and ARIMA model", Decision Support Systems, 107, pp.103~115 (2018), Elsevier BV[DOI]
- Y. Lu, "Time-aware Neural Collaborative Filtering with Multi-dimensional Features on Academic Paper Recommendation", IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (2021)
- Hongzhi Li, "A time-aware hybrid recommendation scheme combining content-based and collaborative filtering", Frontiers of Computer Science, 15(4) (2021), Springer Science and Business Media LLC[DOI]
- 이수정, "사용자 및 항목 기반 통합 유사도의 시간 인지 협력 필터링", 한국컴퓨터정보학회논문지, 27(9), pp.149~155 (2022), 한국컴퓨터정보학회[DOI]
- Y. Wan, "An Integrated Time-Aware Collaborative Filtering Algorithm", Knowledge Management in Organizations, pp.369~379 (2021)