Papers for

ai shopping platform developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Matching agents to objects using features in fast algorithm

Multi-Dimensional Matching

Abstract: We study a matching mechanism where agents and objects are described by features rather than complete rankings. A single spectral projection reduces the problem to a one-dimensional sort, computable in O(N log N) time. We prove that on descaled features and preferences, our algorithm obtains the exact Nash Social Welfare (NSW) optimum within the projected space, with an unconditional utilitarian-welfare guarantee and a conditional NSW guarantee. The proposed mechanism is stable against exogenous noise but not strategy-proof; we provide an explicit profitable misreport. On an agentic AI shopping application, the diagnostics correctly anticipate both a success and a failure case. A 100-instance robustness study confirms the findings.

Thu 24 SeptComputer Science and Game TheoryMachine LearningMultiagent Systems

The gist

Matching people or items usually requires knowing their full preferences, but this can be complicated. This paper studies a way to match agents and objects using just their features, which helps simplify the matching process. The authors use a mathematical method called spectral projection to turn this complex problem into an easier one-dimensional sorting task. Their approach guarantees good outcomes in terms of fairness and overall satisfaction but is not completely safe from people trying to game the system. They tested their method on an AI shopping example and with many random cases to confirm its behavior.

Open → 2609.29958v1