Strategyproof multi-item exchange under single-minded dichotomous preferences

Aziz, Haris

doi:10.1007/s10458-019-09426-w

“…We consider the more general class of submodular valuations, and our main focus is on ex-post fairness. Dichotomous preferences have been further studied extensively in the literature for mechanisms without money (Bogomolnaia, Moulin, and Stong 2005;Freitas 2010;Bouveret and Lang 2008;Kurokawa, Procaccia, and Shah 2018;Ortega 2020), auction design (with private value scaling) (Babaioff, Lavi, and Pavlov 2009;Mishra and Roy 2013) and exchanges (Roth, Sonmez, and Utku Unver 2005;Aziz 2020b).…”

Section: Previous Workmentioning

confidence: 99%

Fair and Truthful Mechanisms for Dichotomous Valuations

Babaioff

¹

,

Ezra

²

,

Feige

³

2021

AAAI

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We consider the problem of allocating a set on indivisible items to players with private preferences in an efficient and fair way. We focus on valuations that have dichotomous marginals, in which the added value of any item to a set is either 0 or 1, and aim to design truthful allocation mechanisms (without money) that maximize welfare and are fair. For the case that players have submodular valuations with dichotomous marginals, we design such a deterministic truthful allocation mechanism. The allocation output by our mechanism is Lorenz dominating, and consequently satisfies many desired fairness properties, such as being envy-free up to any item (EFX), and maximizing the Nash Social Welfare (NSW). We then show that our mechanism with random priorities is envy-free ex-ante, while having all the above properties ex-post. Furthermore, we present several impossibility results precluding similar results for the larger class of XOS valuations.

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“…While the paper of BM focuses on unit-demand valuations, we consider the more general class of submodular valuations, and our focus is on deterministic allocations. Dichotomous preferences have been further studied extensively in the literature for mechanisms without money [11,24,12,30,37], auction design (with private value scaling) [5,34] and exchanges [39,4].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, M L is 1 1+ǫ -EF1 for truthful agents. 4. Each truthful player receives his proportional share in expectation.…”

Section: L Guarantees Every Truthful Agent Hismentioning

confidence: 99%

Fair and Truthful Mechanisms for Dichotomous Valuations

Babaioff¹,

Ezra²,

Feige³

2020

Preprint

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We consider the problem of allocating a set on indivisible items to players with private preferences in an efficient and fair way. We focus on valuations that have dichotomous marginals, in which the added value of any item to a set is either 0 or 1, and aim to design truthful allocation mechanisms (without money) that maximize welfare and are fair. For the case that players have submodular valuations with dichotomous marginals, we design such a deterministic truthful allocation mechanism. The allocation output by our mechanism is Lorenz dominating, and consequently satisfies many desired fairness properties, such as being envy-free up to any item (EFX), and maximizing the Nash Social Welfare (NSW). In contrast, even dropping all fairness requirements, there is no truthful allocation mechanism (without money) that maximizes welfare when players have XOS valuations with dichotomous marginals.To gauge the robustness of our positive results, we also study ǫ-dichotomous valuations, in which the added value of any item to a set is either non-positive, or in the range [1, 1 + ǫ]. We show several impossibility results in this setting, and also a positive result: for players that have additive ǫ-dichotomous valuations with sufficiently small ǫ, we design a randomized truthful mechanism with strong ex-post guarantees. For ρ = 1 1+ǫ , the allocations that it produces generate at least a ρ-fraction of the maximum welfare, and enjoy ρ-approximations for various fairness properties, such as being envy-free up to one item (EF1), and giving each player at least her maximin share.

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“…However, in real practice, users tend to have multiple preferences and diverse interest [2,34]. For example, in the video recommendation streaming, the user may like and accept more than one video recommended by the system.…”

Section: User Simulationmentioning

confidence: 99%

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

Gao,

Li,

Lei

et al. 2022

Preprint

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Recommender systems are usually developed and evaluated on the historical user-item logs. However, most offline recommendation datasets are highly sparse and contain various biases, which hampers the evaluation of recommendation policies. Existing efforts aim to improve the data quality by collecting users' preferences on randomly selected items (e.g., Yahoo! [36] and Coat [47]). However, they still suffer from the high variance issue caused by the sparsely observed data. To fundamentally solve the problem, we present KuaiRec, a fully-observed dataset collected from the social video-sharing mobile App, Kuaishou. The feedback of 1, 411 users on almost all of the 3, 327 videos is explicitly observed. To the best of our knowledge, this is the first real-world fully-observed dataset with millions of user-item interactions in recommendation.To demonstrate the advantage of KuaiRec, we leverage it to explore the key questions in evaluating conversational recommender systems. The experimental results show that two factors in traditional partially-observed data -the data density and the exposure bias -greatly affect the evaluation results. This entails the significance of our fully-observed data in researching many directions in recommender systems, e.g., the unbiased recommendation, interactive/conversational recommendation, and evaluation. We release the dataset and the pipeline implementation for evaluation at https://chongminggao.github.io/KuaiRec/.

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Strategyproof multi-item exchange under single-minded dichotomous preferences

Cited by 12 publications

References 23 publications

Fair and Truthful Mechanisms for Dichotomous Valuations

Fair and Truthful Mechanisms for Dichotomous Valuations

Fair and Truthful Mechanisms for Dichotomous Valuations

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

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