# Ranking with social cues: Integrating online review scores and popularity information

## TL;DR

This study investigates how users combine review scores and popularity when choosing online content, revealing diverse preferences and suggesting potential for personalized ranking systems.

## Contribution

It provides empirical evidence on user preferences for social cues in ranking, highlighting the trade-offs and diversity in decision-making.

## Key findings

- Most users prefer more popular items even with slightly lower scores.
- Significant variation exists in individual preferences for score versus popularity.
- Results suggest opportunities for personalized ranking algorithms.

## Abstract

Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item popularity-that is, the number of users who have experienced an item. These rules, although easy to implement, only partly reflect actual user preferences, as people may assign values to both average scores and popularity and trade off between the two. How do people integrate these two pieces of social information when making choices? We present two experiments in which we asked participants to choose 200 times among options drawn directly from two widely used online venues: Amazon and IMDb. The only information presented to participants was the average score and the number of reviews, which served as a proxy for popularity. We found that most people are willing to settle for items with somewhat lower average scores if they are more popular. Yet, our study uncovered substantial diversity of preferences among participants, which indicates a sizable potential for personalizing ranking schemes that rely on social information.