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A new user similarity model to improve the accuracy of collaborative filtering
Open accessAbstract
Collaborative filtering has become one of the most used approaches to provide personalized services for users. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations for users. However, most approaches related to this approach are based on similarity algorithms, such as cosine, Pearson correlation coefficient, and mean squared difference. These methods are not much effective, especially in the cold user conditions. This paper presents a new user similarity model to improve the recommendation performance when only few ratings are available to calculate the similarities for each user. The model not only considers the local context information of user ratings, but also the global preference of user behavior. Experiments on three real data sets are implemented and compared with many state-of-the-art similarity measures. The results show the superiority of the new similarity model in recommended performance.
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Cites
Content-Based Recommendation Systems
Pazzani, M. J., & Billsus, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72079-9_10 · Cited by 2,600
Cited by
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
Ko, H., Lee, S., Park, Y., & Choi, A. (2022) · Electronics · doi:10.3390/electronics11010141 · Cited by 658
A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data
Patra, B. K., Launonen, R., Ollikainen, V., & Nandi, S. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.03.001 · Cited by 269
Wang, Y., Zhu, J., Liu, R., & Jiang, Y. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102755 · Cited by 43
An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model
Guo, Y., Wang, M., & Li, X. (2017) · Symmetry · doi:10.3390/sym9100216 · Cited by 36
Hasan, M., & Roy, F. (2019) · Big Data and Cognitive Computing · doi:10.3390/bdcc3030039 · Cited by 28
Metadata
- Authors
- Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 56
- Pages
- 156-166
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2003274231
- Cited by
- 604
- References
- 1
- FWCI
- 78.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X. (2014). A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2013.11.006
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