Abstract
Recommendation is a technique which helps and suggests a user, any relevant item from a large information space. Current techniques for this purpose include non-probabilistic methods like content-based filtering and collaborative filtering (CF) and probabilistic methods like Bayesian inference and Case-based reasoning methods. CF algorithms use similarity measures for calculating similarity between users. In this paper, we propose a novel framework which deterministically switches between the CF algorithms based on sparsity to improve accuracy of recommendation.
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Acknowledgements
The authors express their thanks to GroupLens and Jester team who compiled excellent datasets for research on recommender systems. We are grateful to the Principal of our college The National Institute of Engineering, Mysuru for encouraging us and providing the required facility.
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Bhat, A., Kamath, D.M., Anitha, C. (2019). A Novel Deterministic Framework for Non-probabilistic Recommender Systems. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_8
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