22/09/2020

Debiasing item-to-item recommendations with small annotated datasets

Tobias Schnabel, Paul N. Bennett

Keywords: neural networks, datasets, gaze detection, text tagging

Abstract: Item-to-item recommendation (e.g., “People who like this also like...”) is a ubiquitous and important type of recommendation in real-world systems. Observational data from historical interaction logs abound in these settings. However, since virtually all observational data exhibit biases, such as time-in-inventory or interface biases, it is crucial that recommender algorithms account for these biases. In this paper, we develop a principled approach for item-to-item recommendation based on causal inference and present a practical and highly effective method for estimating the causal parameters from a small annotated dataset. Empirically, we find that our approach substantially improves upon existing methods while requiring only small amounts of annotated data.

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