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Bandit Algorithms for e-Commerce Recommender Systems: Extended Abstract

Published:27 August 2017Publication History

ABSTRACT

We study bandit algorithms for e-commerce recommender systems. The question we pose is whether it is necessary to consider reinforcement learning effects in recommender systems. A key reason to introduce a recommender system for a product page on an e-commerce site is to increase the order value by improving the chance of making an upsale. If the recommender system merely predicts the next purchase, there might be no positive effect at all on the order value, since the recommender system predicts sales that would have happened independent of the recommender system. What we really are looking for are the false negatives, i.e., purchases that happen as a consequence of the recommender system. These purchases entail the entire uplift and should be present as reinforcement learning effects. This effect cannot be displayed in a simulation of the site, since there are no reinforcement learning effects present in a simulation. The attribution model must capture the uplift to guarantee an increased order value. However, such an attribution model is not practical, due to data sparsity. Given this starting point, we study some standard attribution models for e-commerce recommender systems, and describe how these fare when applied in a reinforcement learning algorithm, both in a simulation and on live sites.

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            • Published in

              cover image ACM Conferences
              RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems
              August 2017
              466 pages
              ISBN:9781450346528
              DOI:10.1145/3109859

              Copyright © 2017 Owner/Author

              Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 27 August 2017

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              Acceptance Rates

              RecSys '17 Paper Acceptance Rate26of125submissions,21%Overall Acceptance Rate254of1,295submissions,20%

              Upcoming Conference

              RecSys '24
              18th ACM Conference on Recommender Systems
              October 14 - 18, 2024
              Bari , Italy

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