18,435 research outputs found
Your Preference or Mine? A Randomized Field Experiment on Recommender Systems in Two-sided Matching Markets
The literature on recommender systems mainly focuses on product recommendation where buyer’s preferences are considered. However, for user recommendation in two-sided matching markets, potential matches’ preferences may also play a role in focal user’s decision-making. Hence, we seek to understand the impact of providing potential candidates’ preference in such settings. In collaboration with an online dating platform, we design and conduct a randomized field experiment and present users with recommendations based on i) their own preferences, ii) potential matches’ preferences, or iii) mutual preferences. Interestingly, we find that users are sensitive to the provision of potential candidates’ preferences, and they proactively reach out to those “who might prefer them” despite those candidates’ relatively lower desirability. This leads to a greater improvement in matching. The findings provide valuable insights on how to design user recommendation systems beyond the current practice of recommendations based on focal user’s preferences
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
PrivateJobMatch: A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment
Coordination failure reduces match quality among employers and candidates in
the job market, resulting in a large number of unfilled positions and/or
unstable, short-term employment. Centralized job search engines provide a
platform that connects directly employers with job-seekers. However, they
require users to disclose a significant amount of personal data, i.e., build a
user profile, in order to provide meaningful recommendations. In this paper, we
present PrivateJobMatch -- a privacy-oriented deferred multi-match recommender
system -- which generates stable pairings while requiring users to provide only
a partial ranking of their preferences. PrivateJobMatch explores a series of
adaptations of the game-theoretic Gale-Shapley deferred-acceptance algorithm
which combine the flexibility of decentralized markets with the intelligence of
centralized matching. We identify the shortcomings of the original algorithm
when applied to a job market and propose novel solutions that rely on machine
learning techniques. Experimental results on real and synthetic data confirm
the benefits of the proposed algorithms across several quality measures. Over
the past year, we have implemented a PrivateJobMatch prototype and deployed it
in an active job market economy. Using the gathered real-user preference data,
we find that the match-recommendations are superior to a typical decentralized
job market---while requiring only a partial ranking of the user preferences.Comment: 45 pages, 28 figures, RecSys 201
An IPW-based Unbiased Ranking Metric in Two-sided Markets
In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial
for prioritizing items from biased implicit user feedback, such as click data.
Several techniques, such as Inverse Propensity Weighting (IPW), have been
proposed for single-sided markets. However, less attention has been paid to
two-sided markets, such as job platforms or dating services, where successful
conversions require matching preferences from both users. This paper addresses
the complex interaction of biases between users in two-sided markets and
proposes a tailored LTR approach. We first present a formulation of feedback
mechanisms in two-sided matching platforms and point out that their implicit
feedback may include position bias from both user groups. On the basis of this
observation, we extend the IPW estimator and propose a new estimator, named
two-sided IPW, to address the position bases in two-sided markets. We prove
that the proposed estimator satisfies the unbiasedness for the ground-truth
ranking metric. We conducted numerical experiments on real-world two-sided
platforms and demonstrated the effectiveness of our proposed method in terms of
both precision and robustness. Our experiments showed that our method
outperformed baselines especially when handling rare items, which are less
frequently observed in the training data
Private Pareto Optimal Exchange
We consider the problem of implementing an individually rational,
asymptotically Pareto optimal allocation in a barter-exchange economy where
agents are endowed with goods and have preferences over the goods of others,
but may not use money as a medium of exchange. Because one of the most
important instantiations of such economies is kidney exchange -- where the
"input"to the problem consists of sensitive patient medical records -- we ask
to what extent such exchanges can be carried out while providing formal privacy
guarantees to the participants. We show that individually rational allocations
cannot achieve any non-trivial approximation to Pareto optimality if carried
out under the constraint of differential privacy -- or even the relaxation of
\emph{joint} differential privacy, under which it is known that asymptotically
optimal allocations can be computed in two-sided markets, where there is a
distinction between buyers and sellers and we are concerned only with privacy
of the buyers~\citep{Matching}. We therefore consider a further relaxation that
we call \emph{marginal} differential privacy -- which promises, informally,
that the privacy of every agent is protected from every other agent so long as does not collude or share allocation information with other
agents. We show that, under marginal differential privacy, it is possible to
compute an individually rational and asymptotically Pareto optimal allocation
in such exchange economies
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