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New recommender system learns user risk attitudes from clicks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed IncVNM, an interactive recommender system designed to learn user-specific von Neumann-Morgenstern utility functions. This system processes ordinal click feedback from user browsing to update utility estimates, balancing exploration and exploitation. IncVNM aims to improve recommendations for products with random outcomes by considering users' risk attitudes and outcome distributions, outperforming several baseline methods in experiments. AI

IMPACT This research could lead to more personalized and effective recommender systems, particularly for products with uncertain outcomes.

RANK_REASON The cluster contains a research paper detailing a new algorithm for recommender systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New recommender system learns user risk attitudes from clicks

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The cluster contains a research paper detailing a new algorithm for recommender systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jia Liu ·

    IncVNM: An Interactive Recommender System with Incremental von Neumann-Morgenstern Utility Learning

    In modern recommender systems, users are often presented with products whose outcomes are random. The values of such products depend on their entire outcome distributions and on users' attitudes toward risk, making them difficult to evaluate using traditional product-level recomm…

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