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Article overview
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Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms | | Wayne Xin Zhao
; Junhua Chen
; Pengfei Wang
; Qi Gu
; Ji-Rong Wen
; | | Date: |
9 Oct 2020 | | Abstract: | 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 both traditional and neural methods) and
construct extensive experiments on a very large dataset. By carefully
revisiting different options, we make several important findings on the three
factors, which directly provide useful suggestions on how to appropriately set
up the experiments for top-N item recommendation. | | Source: | arXiv, 2010.04484 | | Services: | Forum | Review | PDF | Favorites | |
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