Abstract
Recommender System is useful tool for providing personalized suggestion to users. Collaborative filtering is one of the major and popular techniques for recommender system. Slope One algorithms are simple algorithm based on differential popularity also known as deviation. Slope One algorithms can be used for streaming scenario (dynamically updating parameters while new ratings are coming); however it has few drawbacks. By addressing the drawbacks of Slope One algorithms we proposed a variation of Slope One. Proposed approach uses only those ratings which have more weightage while computing new predicted ratings. We tested our proposed approach in two different popular dataset Netflix and MovieLens datasets. Our approach outperforms the existing Slope One algorithms.
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Das, S., Patra, B.K., Kumar, J. (2021). Weighted Slope One with Threshold Filtering. In: Giri, D., Buyya, R., Ponnusamy, S., De, D., Adamatzky, A., Abawajy, J.H. (eds) Proceedings of the Sixth International Conference on Mathematics and Computing. Advances in Intelligent Systems and Computing, vol 1262. Springer, Singapore. https://doi.org/10.1007/978-981-15-8061-1_12
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DOI: https://doi.org/10.1007/978-981-15-8061-1_12
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