Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems

• URL: http://arxiv.org/abs/2404.13778v1
• Date: Sun, 21 Apr 2024 21:19:31 GMT
• Title: Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems
• Authors: Adilet Yerkin, Elnara Kadyrgali, Yerdauit Torekhan, Pakizar Shamoi,
• Abstract summary: This paper proposes a novel approach to group movie suggestions by examining emotions from three different channels. We employ the Jaccard similarity index to match each participant's emotional preferences to prospective movie choices. The group's consensus level is calculated using a fuzzy inference system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Watching movies is one of the social activities typically done in groups. Emotion is the most vital factor that affects movie viewers' preferences. So, the emotional aspect of the movie needs to be determined and analyzed for further recommendations. It can be challenging to choose a movie that appeals to the emotions of a diverse group. Reaching an agreement for a group can be difficult due to the various genres and choices. This paper proposes a novel approach to group movie suggestions by examining emotions from three different channels: movie descriptions (text), soundtracks (audio), and posters (image). We employ the Jaccard similarity index to match each participant's emotional preferences to prospective movie choices, followed by a fuzzy inference technique to determine group consensus. We use a weighted integration process for the fusion of emotion scores from diverse data types. Then, group movie recommendation is based on prevailing emotions and viewers' best-loved movies. After determining the recommendations, the group's consensus level is calculated using a fuzzy inference system, taking participants' feedback as input. Participants (n=130) in the survey were provided with different emotion categories and asked to select the emotions best suited for particular movies (n=12). Comparison results between predicted and actual scores demonstrate the efficiency of using emotion detection for this problem (Jaccard similarity index = 0.76). We explored the relationship between induced emotions and movie popularity as an additional experiment, analyzing emotion distribution in 100 popular movies from the TMDB database. Such systems can potentially improve the accuracy of movie recommendation systems and achieve a high level of consensus among participants with diverse preferences.

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