Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment

Ruixuan Sun, Avinash Akella, Ruoyan Kong, Moyan Zhou, Joseph A. Konstan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Recommender systems often struggle to strike a balance between matching users’ tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users’ preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don’t like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different levels of exploration control and users’ subjective preferences on interfaces are more predictive of their satisfaction with the recommender.

Original languageEnglish (US)
JournalInternational journal of human-computer interaction
DOIs
StateAccepted/In press - 2023

Bibliographical note

Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.

Keywords

  • Human-recommender interaction
  • interactive recommendation
  • pigeonholing
  • user exploration

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