Conference Proceedings
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi, K Chaudhuri (ed.), R Salakhutdinov (ed.)
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97 | JMLR-JOURNAL MACHINE LEARNING RESEARCH | Published : 2019
Abstract
In recommender systems, usually the ratings of a user to most items are missing and a critical problem is that the missing ratings are often missing not at random (MNAR) in reality. It is widely acknowledged that MNAR ratings make it difficult to accurately predict the ratings and unbiasedly estimate the performance of rating prediction. Recent approaches use imputed errors to recover the prediction errors for missing ratings, or weight observed ratings with the propensities of being observed. These approaches can still be severely biased in performance estimation or suffer from the variance of the propensities. To overcome these limitations, we first propose an estimator that integrates the..
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Awarded by Australian Research Council
Funding Acknowledgements
This work is supported by Australian Research Council Future Fellowship Project FT120100832 and Discovery Project DP180102050. We would like to thank the anonymous reviewers for their insightful comments on the paper.