@inproceedings{schnabel2020debiasing,
author = {Schnabel, Tobias and Bennett, Paul},
title = {Debiasing Item-to-Item Recommendations With Small Annotated Datasets},
organization = {ACM},
booktitle = {RecSys},
year = {2020},
month = {September},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/debiasing-item-to-item-recommendations-with-small-annotated-datasets/},
}