Similarity Measure based on Utilization of Rating Distributions for Data Sparsity Problem in Collaborative Filtering
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Memory-based collaborative filtering is one of the representative types of the recommender system, but it suffers from the inherent problem of data sparsity. Although many works have been devoted to solving this problem, there is still a request for more systematic approaches to the problem. This study exploits distribution of user ratings given to items for computing similarity. All user ratings are utilized in the proposed method, compared to previous ones which use ratings for only common items between users. Moreover, for similarity computation, it takes a global view of ratings for items by reflecting other users’ ratings for that item. Performance is evaluated through experiments and compared to that of other relevant methods. The results reveal that the proposed demonstrates superior performance in prediction and rank accuracies. This improvement in prediction accuracy is as high as 2.6 times more than that achieved by the state-of-the-art method over the traditional similarity measures.
Abstract
Memory-based collaborative filtering is one of the representative types of the recommender system, but it suffers from the inherent problem of data sparsity. Although many works have been devoted to solving this problem, there is still a request for more systematic approaches to the problem. This study exploits distribution of user ratings given to items for computing similarity. All user ratings are utilized in the proposed method, compared to previous ones which use ratings for only common items between users. Moreover, for similarity computation, it takes a global view of ratings for items by reflecting other users’ ratings for that item. Performance is evaluated through experiments and compared to that of other relevant methods. The results reveal that the proposed demonstrates superior performance in prediction and rank accuracies. This improvement in prediction accuracy is as high as 2.6 times more than that achieved by the state-of-the-art method over the traditional similarity measures.
참고문헌 (16)
- 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]
- Mohammad Aamir, "Recommendation System: State of the Art Approach", International Journal of Computer Applications, 120(12), pp.25~32 (2015), Foundation of Computer Science[DOI]
- Sven Kosub, "A note on the triangle inequality for the Jaccard distance", Pattern Recognition Letters, 120, pp.36~38 (2019), Elsevier BV[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]
- 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]
- Shuang-Bo Sun, "Integrating Triangle and Jaccard similarities for recommendation", PLOS ONE, 12(8), pp.e0183570~ (2017), Public Library of Science (PLoS)[DOI]
- Junpeng Guo, "An efficient and accurate recommendation strategy using degree classification criteria for item-based collaborative filtering", Expert Systems with Applications, 164, pp.113756~ (2021), Elsevier BV[DOI]
- Yi Mu, "An Efficient Similarity Measure for Collaborative Filtering", Procedia Computer Science, 147, pp.416~421 (2019), Elsevier BV[DOI]
- Sujoy Bag, "An efficient recommendation generation using relevant Jaccard similarity", Information Sciences, 483, pp.53~64 (2019), Elsevier BV[DOI]
- Bidyut Kr. Patra, "A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data", Knowledge-Based Systems, 82, pp.163~177 (2015), 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]
- 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]
- Jiangzhou Deng, "A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse data", Journal of Information Science, 45(5), pp.656~675 (2018), SAGE Publications[DOI]
- Yong Wang, "A new item similarity based on α-divergence for collaborative filtering in sparse data", Expert Systems with Applications, 166, pp.114074~ (2021), Elsevier BV[DOI]
- Yong Wang, "A hybrid user similarity model for collaborative filtering", Information Sciences, 418-419, pp.102~118 (2017), Elsevier BV[DOI]