Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations

TL;DR

This paper proposes a fair voting-based mechanism for top-K recommendations that accounts for silent users and resists manipulation, improving user satisfaction and fairness in crowdsourced recommendation systems.

Contribution

It introduces a novel adaptation of the Single Transferable Vote method to top-K recommendations, addressing fairness and manipulation issues in crowdsourced data.

Findings

Maximized user satisfaction in recommendations.

Reduced promotion of disliked items by hyper-active users.

Demonstrated effectiveness on real-world datasets.

Abstract

To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users.