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Computer Science > Information Retrieval

arXiv:2109.05278 (cs)
[Submitted on 11 Sep 2021 (v1), last revised 16 Dec 2021 (this version, v2)]

Title:Existence conditions for hidden feedback loops in online recommender systems

Authors:Anton S. Khritankov, Anton A. Pilkevich
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Abstract:We explore a hidden feedback loops effect in online recommender systems. Feedback loops result in degradation of online multi-armed bandit (MAB) recommendations to a small subset and loss of coverage and novelty. We study how uncertainty and noise in user interests influence the existence of feedback loops. First, we show that an unbiased additive random noise in user interests does not prevent a feedback loop. Second, we demonstrate that a non-zero probability of resetting user interests is sufficient to limit the feedback loop and estimate the size of the effect. Our experiments confirm the theoretical findings in a simulated environment for four bandit algorithms.
Comments: 6 pages, 3 figures
Subjects: Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2109.05278 [cs.IR]
  (or arXiv:2109.05278v2 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2109.05278
Journal reference: WISE 2021 Conf., LNCS, V.13081, pp. 267-274. Springer, 2021
Related DOI: https://doi.org/10.1007/978-3-030-91560-5_19

Submission history

From: Anton Khritankov [view email]
[v1] Sat, 11 Sep 2021 13:30:08 UTC (1,109 KB)
[v2] Thu, 16 Dec 2021 15:53:25 UTC (1,109 KB)
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