Abstract
With the rapid growth of network data and demands of users, the concept of AI in recommendation system has become a hot academic topic. However, in sparse data, it is difficult for the current user to obtain his efficient neighbors and for some cold-start users, it doesn’t do anything. Therefore, we constructed a new measure of trust between users for neighborhood based on Collaborative filtering(CF) which uses a pair of users common ratings and exploits Bhattacharyya similarity to finds relevance of each pair of rated items. We also have measured the validity of the proposed model through accuracy, recall rate and F1 measures. The results show that although some recall rates will be lost, the precision is greatly improved. Overall, it achieved good results.
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Acknowledgement
The paper is funded by the National Natural Science Foundation of Grant No. 61272036. Meanwhile, it is also funded by the Central University Fundamental Research Fund and the Key Discipline of Shanghai Second Polytechnic University. The grant numbers are NZ2013306 and XXKZD1604 respectively.
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Qin, X., Tan, W., Tang, A. (2019). A New Trust-Based Collaborative Filtering Measure Using Bhattacharyya Coefficient. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_31
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DOI: https://doi.org/10.1007/978-981-15-1377-0_31
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