Towards a Causal Decision-Making Framework for Recommender Systems-Reference-Cited by-同舟云学术

Towards a Causal Decision-Making Framework for Recommender Systems

Published:2023-10-26 Issue: Volume: Page:
ISSN:2770-6699
Container-title:ACM Transactions on Recommender Systems
language:en
Short-container-title:ACM Trans. Recomm. Syst.

Author:

Cavenaghi Emanuele¹,Zanga Alessio²,Stella Fabio³,Zanker Markus⁴

Affiliation:

1. Free University of Bozen-Bolzano, Italy

2. University of Milano-Bicocca, Italy and F. Hoffmann - La Roche Ltd, Switzerland

3. University of Milano-Bicocca, Italy

4. Free University of Bozen-Bolzano, Italy and University of Klagenfurt, Austria

Abstract

Abstract. Causality is gaining more and more attention in the machine learning community and consequently also in recommender systems research. The limitations of learning offline from observed data are widely recognized, however, applying debiasing strategies like Inverse Propensity Weighting does not always solve the problem of making wrong estimates. This concept paper contributes a summary of debiasing strategies in recommender systems and the design of several toy examples demonstrating the limits of these commonly applied approaches. Therefore, we propose to map the causality frameworks of potential outcomes and structural causal models onto the recommender systems domain in order to foster future research and development. For instance, applying causal discovery strategies on offline data to learn the causal graph in order to compute counterfactuals or improve debiasing strategies.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3629169

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