Summary of Multi-channel Emotion Analysis For Consensus Reaching in Group Movie Recommendation Systems, by Adilet Yerkin et al.
Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems
by Adilet Yerkin, Elnara Kadyrgali, Yerdauit Torekhan, Pakizar Shamoi
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to group movie suggestions is proposed by examining emotions from three different channels: movie descriptions, soundtracks, and posters. The Jaccard similarity index is employed to match each participant’s emotional preferences to prospective movie choices, followed by a fuzzy inference technique to determine group consensus. A weighted integration process fuses emotion scores from diverse data types, and recommendations are based on prevailing emotions and viewers’ best-loved movies. The study compares predicted and actual scores, demonstrating the efficiency of using emotion detection for this problem (Jaccard similarity index = 0.76). Additionally, the relationship between induced emotions and movie popularity is explored. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Movies can be hard to choose because different people like different things. This paper tries to make it easier by looking at three things: what the movie says, how it sounds, and what’s on the poster. It uses a special way of comparing emotions to find movies that most people will like. The study shows that this method works pretty well (Jaccard similarity index = 0.76). It also looks at why some popular movies are so loved. |
Keywords
» Artificial intelligence » Inference
