Listwise Recommendation Approach with Non-negative Matrix Factorization
Pandey, G., & Wang, S. (2018). Listwise Recommendation Approach with Non-negative Matrix Factorization. In I. Czarnowski, R. J. Howlett, L. C. Jain, & L. Vlacic (Eds.), KES-IDT 2018 : Proceedings of the 10th International KES Conference on Intelligent Decision Technologies (pp. 22-32). Springer. Smart Innovation, Systems and Technologies, 97. https://doi.org/10.1007/978-3-319-92028-3_3
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Smart Innovation, Systems and TechnologiesDate
2018Copyright
© Springer International Publishing AG, part of Springer Nature 2019
Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predict users’ possible rankings on all items. For this, we present LwRec, a novel listwise ranking-oriented matrix factorization algorithm. It aims to predict the missing values in the user-ranking probability matrix, aiming that each row of the final predicted matrix should have a probability distribution similar to the original one. Extensive offline experiments on two benchmark datasets against several state-of-the-art baselines demonstrate the effectiveness of our proposal.
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SpringerParent publication ISBN
978-3-319-92027-6Conference
International KES Conference on Intelligent Decision TechnologiesIs part of publication
KES-IDT 2018 : Proceedings of the 10th International KES Conference on Intelligent Decision TechnologiesISSN Search the Publication Forum
2190-3018Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/28131081
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