Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912362365190144 |
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| author | Ahmad, Juan Hellgren, Jonas Said, Alan |
| author_facet | Ahmad, Juan Hellgren, Jonas Said, Alan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations Ahmad, Juan Hellgren, Jonas Said, Alan Information Retrieval Human-Computer Interaction 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. |
| title | Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations |
| topic | Information Retrieval Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.03376 |