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Published in: Social Choice and Welfare 4/2023

25-07-2023 | Original Paper

Tailored recommendations on a matching platform

Author: Gunhaeng Lee

Published in: Social Choice and Welfare | Issue 4/2023

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Abstract

Matching platforms not only mediate matches but also work as information gatekeepers. When users with private tastes use such a platform to find a partner, the platform asks them to provide match-relevant information; subsequently, it aggregates and distributes the collected data back to each user to facilitate the effective coordination of matches. This study aims to examine how such a platform can design its information flow to make users form matches in a way that is desirable for the platform. I characterize a form of two-way communication that employs both verifiable and non-verifiable messages; then, I delineate the conditions under which a platform can (cannot) achieve its ex-post optimal matching outcome. On a platform that achieves such an outcome, users would fully reveal their private tastes, but the platform would return personalized and only filtered information back to each user in the form of a “recommendation.” I identify three key factors that enable such communication, namely (1) the distance between the distribution of tastes of each side; (2) the uncertainty measure of each distribution; and (3) the population size. As applications, I first study the markets with costly verifiable information and propose a sufficient condition that achieves the optimal matching outcome. Then, I study a two-way communication protocol with non-verifiable messages and demonstrate that communication strictly improves efficiency under any circumstances.

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Appendix
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Footnotes
1
Examples are Taskrabbit, Careerbuilder, and Airbnb.
 
2
For example, at Freelancer.com, one of the largest online job-matching platforms, job seekers are required to upload their résumés and firms need to specify details about their job openings. After collecting and aggregating information from users, the platform presents recommended job opportunities to users.
 
3
Users cannot lie about their information; however, they can choose what information to disclose and what to withhold; an example of this type of message in the present context is a résumé.
 
4
By contrast, in the literature on communication games (e.g., cheap talk and Bayesian persuasion), one party—often referred to as a “sender”—possesses relevant information, while the other party—the “receiver”—is the one who takes an action.
 
5
Each user’s private observation of his or her location.
 
6
The concept of complementarity is defined through the notion of increasing difference For types \(\theta\) and \(\omega\), the two types exhibit complementarity under a match function v if \(v(\theta ',\omega ')-v(\theta ',\omega )\ge v(\theta ,\omega ')-v(\theta ,\omega ),\forall \theta '\ge \theta , \forall \omega '\ge \omega\).
 
7
Uniform distribution has the highest variance among unimodal distributions over the unit interval.
 
8
The example continues to the example presented after Proposition 1. In the example, it is shown that the platform indeed correctly infers the types in equilibrium.
 
9
The model can be used to study matching platforms targeting other types of users. I use the worker-firm terminology, although consumer-service provider or man–woman pairings could be equally relevant.
 
10
e.g., skill or area of specialization.
 
11
For example, Taskrabbit.com charges a 15% service fee on each wage paid to the worker, while Freelancer.com levies a fee of 3% to the employer and 10% to the worker in a similar manner.
 
12
Many online platforms provide “instant match” options to users. Upon a user’s request, the platform directly provides a match with another user on the other side of the market. Even if the platform matches users, it allows users to cancel the request after the match has been provided. For example, on TaskRabbit, the “Quick Assign” option provides an instant match, which can be canceled either if users do not engage in any further actions regarding the match within two hours or if the assigned user cancels the match.
 
13
e.g., The message \(m_i=[a_i,b_i]\) means that the true location of i lies inside of the interval.
 
14
Just for an illustration purpose, let us relax the assumption that G and F are strictly increasing in this example. I can easily find examples with the same result without the relaxation of the assumption. However, it requires more involved calculation. For example, one can check that \(F(\theta )=\theta\) and \(G(\omega )=\omega ^3\) also works well with this example.
 
15
The detailed derivation can be found in the proof of Theorem 1.
 
16
Note that the optimal matching may not be socially optimal as it does not consider the cost of certifications.
 
17
\(\theta _j\) has rank k in \({\varvec{\theta }}\) if it is the \(k{\textrm{th}}\) smallest value in \({\varvec{\theta }}\)
 
18
The probability that \(k-1\) firms among \(n-1\) firms are lower than \(\theta _j\), subtracting the probability that k firms among \(n-1\) firms are lower than \(\theta _j\).
 
19
For a proof, see p. 93 of DasGupta (2008).
 
20
Mean of B(mp) is mp.
 
21
The partition is well defined because by continuity of \(v_2(\cdot ,\cdot )\).
 
22
Although the construction of belief is motivated from Hagenbach et al. (2014), the construction cannot directly applied to the model because I deal with continuum type space with non-monotonic relation.
 
23
The same proof applies to the other case.
 
24
R is a totally ordered set as it is a subset of [0, 1]
 
25
The same construction works for workers as well.
 
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Metadata
Title
Tailored recommendations on a matching platform
Author
Gunhaeng Lee
Publication date
25-07-2023
Publisher
Springer Berlin Heidelberg
Published in
Social Choice and Welfare / Issue 4/2023
Print ISSN: 0176-1714
Electronic ISSN: 1432-217X
DOI
https://doi.org/10.1007/s00355-023-01475-1

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