Leveraging Inverse Probability Weighting Method to Missing Not at Random Problem

Document Type

Conference Proceeding

Publication Date



In nowadays recommender system field. The selection bias is ubiquitous to most of the data. Most real-world rating data is sparse and missing not at random (MNAR). MNAR data make it difficult to accurately estimate the performance of prediction model as well as learn an optimal prediction model. Investigation on overcoming the MNAR is becoming the mainstream in recommendation field. Plenty of approaches has been proposed to solve this problem. And many of them make the use of the propensity of observing each user-item event. Many of the approaches are suffering from the variance of the propensity. This paper proposes two approaches, summary of inversed propensity (SIP) and clip to make the propensity calculated by logistic regression more effective. And real-world-dataset based experiment will show the performance of these two approaches.

Publication Title

Journal of Physics: Conference Series



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