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Optimized Item Selection to Boost Exploration for Recommender Systems

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Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12735))

Abstract

Recommender Systems have become the backbone of personalized services that provide tailored experiences to individual users. Still, data sparsity remains a common challenging problem, especially for new applications where training data is limited or not available. In this paper, we formalize a combinatorial problem that is concerned with selecting the universe of items for experimentation with recommender systems. On one hand, a large set of items is desirable to increase the diversity of items. On the other hand, a smaller set of items enable rapid experimentation and minimize the time and the amount of data required to train machine learning models. We show how to optimize for such conflicting criteria using a multi-level optimization framework. Our approach integrates techniques from discrete optimization, unsupervised clustering, and latent text embeddings. Experimental results on well-known movie and book recommendation benchmarks demonstrate the benefits of optimized item selection.

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Notes

  1. 1.

    Thanks to our anonymous reviewer for this suggestion.

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Authors and Affiliations

  1. AI Center of Excellence Fidelity Investments, Boston, USA

    Serdar Kadıoğlu, Bernard Kleynhans & Xin Wang

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  1. Serdar Kadıoğlu
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  2. Bernard Kleynhans
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  3. Xin Wang
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Correspondence to Bernard Kleynhans .

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  1. Monash University, Melbourne, VIC, Australia

    Peter J. Stuckey

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Kadıoğlu, S., Kleynhans, B., Wang, X. (2021). Optimized Item Selection to Boost Exploration for Recommender Systems. In: Stuckey, P.J. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2021. Lecture Notes in Computer Science(), vol 12735. Springer, Cham. https://doi.org/10.1007/978-3-030-78230-6_27

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  • DOI: https://doi.org/10.1007/978-3-030-78230-6_27

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