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| Asıl Yazarlar: | , , , , |
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| Materyal Türü: | Recurso digital |
| Dil: | |
| Baskı/Yayın Bilgisi: |
Zenodo
2026
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| Konular: | |
| Online Erişim: | https://doi.org/10.5281/zenodo.19760680 |
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İçindekiler:
- Abstract - Choosing a movie to watch together is a common yet contentious challenge for groups of friendsand family. Traditional recommendation systems are architected for individual users and fail to account forthe complex dynamics that emerge when multiple people with dif ering tastes, moods, and streamingplatformsubscriptions attempt to reach a consensus. This paper presents Movie Bubble, a group-centric movierecommendation system that bridges this gap by combining individual user profiling, hybridfilteringtechniques (collaborative and content-based), mood-based NLP filtering, and democratic group consensusaggregation strategies. The system organises users into temporary groups called bubbles, aggregates theirpreferences, and generates a fair, inclusive shortlist of movie suggestions. A built-in polling mechanismempowers all group members to vote, ensuring participatory decision- making. Consensus strategies includingaverage aggregation, least misery, and Borda count voting are implemented as backend logic to maximisecollective satisfaction. The system integrates with the TMDb API and TMDB Watch Providers API for up-to-date movie data and streaming availability, and uses PostgreSQL as its sole persistence layer. Resultsdemonstrate that Movie Bubble significantly improves the group movie-selection experience, transformingit from a source of conflict into a collaborative and enjoyable activity.