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2021 | OriginalPaper | Buchkapitel

Causality-Aware Neighborhood Methods for Recommender Systems

verfasst von : Masahiro Sato, Janmajay Singh, Sho Takemori, Qian Zhang

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

The business objectives of recommenders, such as increasing sales, are aligned with the causal effect of recommendations. Previous recommenders targeting for the causal effect employ the inverse propensity scoring (IPS) in causal inference. However, IPS is prone to suffer from high variance. The matching estimator is another representative method in causal inference field. It does not use propensity and hence free from the above variance problem. In this work, we unify traditional neighborhood recommendation methods with the matching estimator, and develop robust ranking methods for the causal effect of recommendations. Our experiments demonstrate that the proposed methods outperform various baselines in ranking metrics for the causal effect. The results suggest that the proposed methods can achieve more sales and user engagement than previous recommenders.

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Vorheriges Kapitel Studying the Effectiveness of Conversational Search Refinement Through User Simulation

Nächstes Kapitel User Engagement Prediction for Clarification in Search

If we focus on ATE or ATT, as often the case in causal inference, the random noise is not a severe problem since it disappears by taking average of large samples. It becomes a problem when we want to rank items by the estimates for each item.

Note that the deployed recommender is different from recommenders that we train and evaluate from \(\{Y_{ui}\}\) and \(\{Z_{ui}\}\), hence we might not have control over previous recommendation logs. In experiment section, we also investigate how different conditions of previous recommendations affect the proposed recommenders.

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Titel: Causality-Aware Neighborhood Methods for Recommender Systems
verfasst von: Masahiro Sato
Janmajay Singh
Sho Takemori
Qian Zhang
Verlag: Springer International Publishing
Buch: Advances in Information Retrieval
Print ISBN: 978-3-030-72112-1

Electronic ISBN: 978-3-030-72113-8

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-72113-8_40

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