Correction to: Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization

Recommender systems have been served to assist decision making by recommending a list of items to the end users. Multi-criteria recommender system (MCRS) is a type of recommender systems which enhance recommendation performance by taking user preferences on multiple criteria. Traditional algorithms for MCRS usually predict user ratings on these criteria, and finally estimate the overall rating by different aggregation functions. In this paper, we propose a new multi-criteria recommendation framework in which we can take advantage of Pareto ranking based estimated preferences on multiple criteria, and infer a ranking score for top-N recommendations. The proposed framework is general enough and all existing algorithms in MCRS can be reused to be integrated with our framework. We demonstrate the effectiveness of the proposed framework by evaluating top-N recommendations over four real-world data sets.

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Correction to: Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization

Abstract: Following the publication of the original article [1], it was noted that due to a typesetting error the Figs. 1 and 2 were swapped. The correct figures and captions have been included in this correction, and the original article has been corrected.

Cited by 8 publications

References 1 publication

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Boosting the optimization of membrane electrode assembly in proton exchange membrane fuel cells guided by explainable artificial intelligence

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Multi-Criteria Ranking: Next Generation of Multi-Criteria Recommendation Framework

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