Abstract
Although the Slope One family of algorithms provides an appealing solution to the scalability problem in collaborative filtering recommendation systems, the data sparsity problem as a major issue still remains open. Many of the recent algorithms rely on sophisticated methods which not only have negative effect on the scalability of Slope One, but also need some additional information extra to ratings matrix. To address these problems in this paper, we have proposed a novel method based on Weighted Slope One algorithm which introduces virtual predictive items in relatively sparse ratings databases. These virtual items are those which neither have rated by active users nor have deviation to active items. The strength of our approach lies in its ability to manage the data sparsity problem without using any extra information. Indeed, it uses the ratings data which are common in collaborative filtering systems. Our proposed algorithm is scalable, easy to implement and updatable on the fly (without changing comprehensively). Experimental results on the MovieLens and Netflix datasets show the effectiveness of the proposed algorithm in handling data sparsity problem. It also outperforms some state-of-the-art collaborative filtering algorithms in terms of prediction quality.
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Saeed, M., Mansoori, E.G. A new slope one based recommendation algorithm using virtual predictive items. J Intell Inf Syst 50, 527–547 (2018). https://doi.org/10.1007/s10844-017-0470-7
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DOI: https://doi.org/10.1007/s10844-017-0470-7