Abstract
Explored an approach, to generate adequate recommendations for the target user. The approach used in this paper focuses on using the listening history of the target user as implicit feedback. In this paper, collaborative filtering approach is used to find set of users that have similar taste in music. The recommendations are evaluated using the past listening history of the target user and finding the most similar users based on the similarity score. Jaccard Mean Squared Difference (JMSD) is used to find the extent to which the users are co-related. Users having the highest similarity scores with respect to the end user will be used to generate refined song recommendations.
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Arora, P., Bindra, A., Chand, A., Dhilod, A., Sharma, A. (2021). Improving Collaborative Filtering Using JMSD on Implicit Feedback. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_39
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DOI: https://doi.org/10.1007/978-3-030-73050-5_39
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