시간 인지 협력 필터링 시스템을 위한 평가 시간과 평가치의 상관 분석
Correlation Analysis between Rating Time and Values for Time-aware Collaborative Filtering Systems
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초록
협력 필터링 시스템에서 추천 리스트의 고객 만족도를 위하여 시스템이 산출하는 항목 평가 예측치는 매우 중요하다. 시간 인지 기반 시스템에서는 사용자들의 평가 시간을 반영하여 예측치를산출하는데, 대개 과거 평가치일수록 기하급수적으로 낮은 가중치를 부여하였다. 본 연구에서는평가치에 대한 평가 시간의 영향력이 다양한 요인에 따라 달라지는지 알아 보기 위하여, 사용자의 평가 적극성 정도, 항목의 인기도, 그리고 항목 장르별로 사용자 평가치와 평가 시간의 상관도를 조사하였다. 두 종류의 공개 데이터셋을 활용한 분석 결과, 특히 희소 데이터셋에서 각 요인에따라 현저히 다른 상관지수 값을 얻었다. 따라서 평가 예측치에 대한 평가 시간의 영향력의 크기는 평가 데이터 밀집도 뿐만 아니라 상기한 여러 가지 요소를 고려하여 다르게 책정되어야 한다는 사실을 확인하였다.
Abstract
In collaborative filtering systems, the item rating prediction values calculated by the systems are very important for customer satisfaction with the recommendation list. In the time-aware system, predictions are calculated by reflecting the rating time of users, and in general, exponentially lower weights are assigned to past rating values. In this study, to find out whether the influence of rating time on the rating value varies according to various factors, the correlation between user rating value and rating time is investigated by the degree of user rating activity, the popularity of items, and item genres. As a result, using two types of public datasets, especially in the sparse dataset, significantly different correlation index values were obtained for each factor. Therefore, it is confirmed that the influence weight of the rating time on the rating prediction value should be set differently in consideration of the above-mentioned various factors as well as the density of the dataset.
참고문헌 (16)
- J. Bobadilla, "Recommender systems survey", Knowledge-Based Systems, 46, pp.109~132 (2013), 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]
- Zeynep Batmaz, "A review on deep learning for recommender systems: challenges and remedies", Artificial Intelligence Review, 52(1), pp.1~37 (2019), Springer Science and Business Media LLC[DOI]
- Amit Livne, "Evolving context-aware recommender systems with users in mind", Expert Systems with Applications, 189(1), pp.116042~ (2022), 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]
- Guangxia Xu, "A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context", Journal of Electrical and Computer Engineering, 2019, pp.1~12 (2019), Hindawi Limited[DOI]
- 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)
- Charinya Wangwatcharakul, "A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution", Expert Systems with Applications, 185(15), pp.115626~ (2021), 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)
- 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]
- 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]
- Y. Wan, "An Integrated Time-Aware Collaborative Filtering Algorithmq", Knowledge Management in Organizations, pp.369~379 (2021)
- Hongzhi Li, "A time-aware hybrid recommendation scheme combining content-based and collaborative filtering", Frontiers of Computer Science, 15(4), pp.154613~ (2021), Springer Science and Business Media LLC[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)
- Chao Tong, "TimeTrustSVD: A collaborative filtering model integrating time, trust and rating information", Future Generation Computer Systems, 93, pp.933~941 (2019), Elsevier BV[DOI]