Using Stable Matching to Optimize the Balance between Accuracy and
Diversity in Recommendation
- URL: http://arxiv.org/abs/2006.03715v1
- Date: Fri, 5 Jun 2020 22:12:25 GMT
- Title: Using Stable Matching to Optimize the Balance between Accuracy and
Diversity in Recommendation
- Authors: Farzad Eskandanian, Bamshad Mobasher
- Abstract summary: Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains.
Attempts to increase aggregate diversity often result in lower recommendation accuracy for end users.
We propose a two-sided post-processing approach in which both user and item utilities are considered.
- Score: 3.0938904602244355
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Increasing aggregate diversity (or catalog coverage) is an important
system-level objective in many recommendation domains where it may be desirable
to mitigate the popularity bias and to improve the coverage of long-tail items
in recommendations given to users. This is especially important in
multistakeholder recommendation scenarios where it may be important to optimize
utilities not just for the end user, but also for other stakeholders such as
item sellers or producers who desire a fair representation of their items
across recommendation lists produced by the system. Unfortunately, attempts to
increase aggregate diversity often result in lower recommendation accuracy for
end users. Thus, addressing this problem requires an approach that can
effectively manage the trade-offs between accuracy and aggregate diversity. In
this work, we propose a two-sided post-processing approach in which both user
and item utilities are considered. Our goal is to maximize aggregate diversity
while minimizing loss in recommendation accuracy. Our solution is a
generalization of the Deferred Acceptance algorithm which was proposed as an
efficient algorithm to solve the well-known stable matching problem. We prove
that our algorithm results in a unique user-optimal stable match between items
and users. Using three recommendation datasets, we empirically demonstrate the
effectiveness of our approach in comparison to several baselines. In
particular, our results show that the proposed solution is quite effective in
increasing aggregate diversity and item-side utility while optimizing
recommendation accuracy for end users.
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