협력 필터링을 위한 공통 평가치 분리 기반의 새로운 유사도 척도
A New Similarity Measure based on Separation of Common Ratings for Collaborative Filtering
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
추천 시스템의 여러 구현 기법들 중 협력 필터링은 과거 평가 이력을 토대로 유사성이 높은 인접 이웃들을 선정하여, 그들이 선호했던 상품들을 추천하는데, 많은 상업 사이트에서 성공적으로활용되고 있다. 유사도의 정확한 측정은 시스템의 성능을 좌우하는 매우 중요한 요소이다. 기존에다양한 방식의 유사도 척도들이 개발되었는데, 대개 전통적인 유사도 척도와 기개발된 여러 계수들과의 통합 방식이었다. 본 연구에서는 새로운 방식의 유사도 척도를 제안한다. 두 사용자 간의공통 평가 영역을 평가치 크기에 따라 분할하여 각 부분 영역별로 유사도를 측정하고 이들을 가중 통합함으로써, 유사한 영역이 구체적으로 파악되어 최종 유사도값에 반영된다. 두 종류의 개방형 데이터셋을 활용한 성능을 측정하였고, 그 결과 특히 밀집 데이터셋에서 제안 방법의 예측 정확도, 순위 정확도, 평균 정밀도 성능이 기존보다 우수하였다. 제안 척도는 다양한 상업 시스템에서 사용자들의 선호에 보다 적합한 상품을 추천하는데 유용하게 활용될 것으로 기대한다.
Abstract
Among various implementation techniques of recommender systems, collaborative filtering selects nearest neighbors with high similarity based on past rating history, recommends products preferred by them, and has been successfully utilized by many commercial sites. Accurate estimation of similarity is an important factor that determines performance of the system. Various similarity measures have been developed, which are mostly based on integrating traditional similarity measures and several indices already developed. This study suggests a similarity measure of a novel approach. It separates the common rating area between two users by the magnitude of ratings, estimates similarity for each subarea, and integrates them with weights. This enables identifying similar subareas and reflecting it onto a final similarity value. Performance evaluation using two open datasets is conducted, resulting in that the proposed outperforms the previous one in terms of prediction accuracy, rank accuracy, and mean average precision especially with the dense dataset. The proposed similarity measure is expected to be utilized in various commercial systems for recommending products more suited to user preference.
참고문헌 (21)
- Mahdi Jalili, "Evaluating Collaborative Filtering Recommender Algorithms: A Survey", IEEE Access, 6, pp.74003~74024 (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]
- Sujoy Bag, "An efficient recommendation generation using relevant Jaccard similarity", Information Sciences, 483, pp.53~64 (2019), Elsevier BV[DOI]
- Shuang-Bo Sun, "Integrating Triangle and Jaccard similarities for recommendation", PLOS ONE, 12(8), pp.e0183570~ (2017), Public Library of Science (PLoS)[DOI]
- J. Bobadilla, "A new collaborative filtering metric that improves the behavior of recommender systems", Knowledge-Based Systems, 23(6), pp.520~528 (2010), Elsevier BV[DOI]
- Sven Kosub, "A note on the triangle inequality for the Jaccard distance", Pattern Recognition Letters, 120, pp.36~38 (2019), 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)[DOI]
- Bo Zhu, "An Efficient Recommender System Method Based on the Numerical Relevances and the Non-Numerical Structures of the Ratings", IEEE Access, 6, pp.49935~49954 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Arta Iftikhar, "An Improved Product Recommendation Method for Collaborative Filtering", IEEE Access, 8, pp.123841~123857 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Yi Mu, "An Efficient Similarity Measure for Collaborative Filtering", Procedia Computer Science, 147, pp.416~421 (2019), Elsevier BV[DOI]
- S. Lee, "Improving Jaccard Index using Genetic Algorithms for Collaborative Filtering", Lecture Notes on Computer Science, 10385, pp.378~385 (2017)[DOI]
- Mohammad Yahya H. Al-Shamri, "Power coefficient as a similarity measure for memory-based collaborative recommender systems", Expert Systems with Applications, 41(13), pp.5680~5688 (2014), Elsevier BV[DOI]
- Hao Chen, "An improved collaborative recommendation algorithm based on optimized user similarity", The Journal of Supercomputing, 72(7), pp.2565~2578 (2016), Springer Science and Business Media LLC[DOI]
- Dawei Wang, "Improving neighbor-based collaborative filtering by using a hybrid similarity measurement", Expert Systems with Applications, 160, pp.113651~ (2020), Elsevier BV[DOI]
- Ankush Jain, "Multi-objective item evaluation for diverse as well as novel item recommendations", Expert Systems with Applications, 139, pp.112857~ (2020), Elsevier BV[DOI]
- Yilmaz Ar, "A genetic algorithm solution to the collaborative filtering problem", Expert Systems with Applications, 61, pp.122~128 (2016), Elsevier BV[DOI]
- Gang Lv, "Research on recommender system based on ontology and genetic algorithm", Neurocomputing, 187, pp.92~97 (2016), Elsevier BV[DOI]
- Nour El Islem Karabadji, "Improving memory-based user collaborative filtering with evolutionary multi-objective optimization", Expert Systems with Applications, 98, pp.153~165 (2018), Elsevier BV[DOI]
- Rahim Rashidi, "Proposing improved meta-heuristic algorithms for clustering and separating users in the recommender systems", Electronic Commerce Research (2021), Springer Science and Business Media LLC[DOI]
- Majid Hatami, "Improving Results and Performance of Collaborative Filtering-based Recommender Systems using Cuckoo Optimization Algorithm", International Journal of Computer Applications, 88(16), pp.46~51 (2014), Foundation of Computer Science[DOI]
- L. Baltrunas, "Group Recommendation with Rank Aggregation and Collaborative Filtering", Proceedings of the ACM Conference on Recommender Systems, pp.119~126 (2010)