Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations

TL;DR

This study compares user perceptions of positive-only versus mixed explanations in movie recommendations, finding that simpler positive explanations are generally preferred in entertainment contexts, influencing trust and satisfaction.

Contribution

It provides empirical evidence on how explanation style impacts user perceptions in recommender systems, emphasizing the effectiveness of positive explanations in low-stakes scenarios.

Findings

Positive explanations received higher trust and satisfaction ratings.

Explanation style significantly affects perceived transparency and effectiveness.

Results suggest context-dependent preferences for explanation types.

Abstract

Recommender systems play a vital role in helping users discover content in streaming services, but their effectiveness depends on users understanding why items are recommended. In this study, explanations were based solely on item features rather than personalized data, simulating recommendation scenarios. We compared user perceptions of one-sided (purely positive) and two-sided (positive and negative) feature-based explanations for popular movie recommendations. Through an online study with 129 participants, we examined how explanation style affected perceived trust, transparency, effectiveness, and satisfaction. One-sided explanations consistently received higher ratings across all dimensions. Our findings suggest that in low-stakes entertainment domains such as popular movie recommendations, simpler positive explanations may be more effective. However, the results should be interpreted with caution due to potential confounding factors such as item familiarity and the placement of negative information in explanations. This work provides practical insights for explanation design in recommender interfaces and highlights the importance of context in shaping user preferences.