Abstract
The accuracy of the recommendation process based on neighborhood-based collaborative filtering tends to diverge because the interests/preferences of the neighbors are likely to change along with time. The traditional recommendation methods do not consider the shifted likings of the neighbors; hence, the calculated set of neighbors does not always reflect the optimal neighborhood at any given point of time. In this paper, we propose a novel approach to calculate the similarity between users and find the similar neighbors of the target user in different time period to improve the accuracy in personalized recommendation. The performance of the proposed algorithm is tested on the MovieLens dataset using different performance metrics viz. MAE, RMSE, precision, recall, F-score, and accuracy.
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MovieLens | GroupLens: https://grouplens.org/datasets/movielens/. Last Accessed 18 Aug 2018
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Singh, P.K., Setta, S., Pramanik, P.K.D., Choudhury, P. (2020). Improving the Accuracy of Collaborative Filtering-Based Recommendations by Considering the Temporal Variance of Top-N Neighbors. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_1
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DOI: https://doi.org/10.1007/978-981-15-1286-5_1
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