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Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
[article]
2020
arXiv
pre-print
Top-N item recommendation has been a widely studied task from implicit feedback. Although much progress has been made with neural methods, there is increasing concern on appropriate evaluation of recommendation algorithms. In this paper, we revisit alternative experimental settings for evaluating top-N recommendation algorithms, considering three important factors, namely dataset splitting, sampled metrics and domain selection. We select eight representative recommendation algorithms (covering
arXiv:2010.04484v1
fatcat:y4pnzgkucff4hl3paujpmbvr24