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Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
[article]
2020
arXiv
pre-print
The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user behavior data with pointwise or pairwise loss, makes the model biased towards popular items. This results in the terrible Matthew effect, making popular items be more frequently recommended and become even more popular. Existing work addresses this issue with Inverse
arXiv:2010.15363v1
fatcat:oqazsv33qres5kssf6lsb4fply