Abstract
Nowadays, Internet services and products are increasingly abundant, and efficient and reliable recommender systems become increasingly important and have been widely accepted by users. Item-based collaborative filtering (CF) is one of the most popular techniques for determining recommendations. A common problem of traditional item-based CF approaches is that they only consider ratings of co-rated users when computing item similarities, which are likely to ignore relationships between items and result in unreliable relationships between different items. To improve the quality of recommendation, this paper proposes a new scheme of similarity measurement between items, computing the similarity based on the preference distribution of global users. In this paper, a data model for denoting the relationships among items and an SKL-based item similarities computing approach are proposed. Finally, experimental results show that the proposed approach can make better recommendation results compared with classical item-based CF approaches.
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Acknowledgments
This work is partially supported by NSFC (Nos. 61003054 and 61170020), College of Natural Science Research Project of Jiangsu Province (No. 10KJB520018), Science and Technology Support Program of Suzhou (No. SG201257), Science and Technology Support Program of Jiangsu Province (No. BE2012075), and Open Fund of Jiangsu Province Software Engineering R&D Center (SX201205). This work is also partially supported by the Natural Science Foundation of China under grant Nos. 61003054 and 61170020, Jiangsu Province Colleges and Universities of Natural Science Research Project under grant Nos. 10KJB520018 and 13KJB520021, Jiangsu Province Science and Technology Support Program under grant No. BE2012075, and Suzhou City Science and Technology Support Program under grant No. SG201257.
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Li, C., Zhao, P., Wu, J., Mao, J., Cui, Z. (2015). An Item-Based Collaborative Filtering Framework Based on Preferences of Global Users. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_128
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DOI: https://doi.org/10.1007/978-3-319-11104-9_128
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