Using Affective Features from Media Content Metadata for Better Movie Recommendations

TL;DR

This paper proposes an emotion-aware movie recommendation system that uses affective features extracted from movie overviews and user viewing history to improve recommendation relevance.

Contribution

It introduces a novel method of assigning affective tags to movies using a transfer learning-based emotion detection model and demonstrates improved recommendation ranking with cosine similarity.

Findings

Cosine similarity outperforms other distance metrics in ranking accuracy.

Affective features from movie overviews enhance recommendation relevance.

Emotion-aware reranking improves user satisfaction with recommendations.

Abstract

This paper investigates the causality in the decision making of movie recommendations through the users' affective profiles. We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview. We apply a text-based Emotion Detection and Recognition model, which trained by tweets short messages and transfers the learned model to detect movie overviews' implicit affective features. We vectorized the affective movie tags to represent the mood embeddings of the movie. We obtain the user's emotional features by taking the average of all the movies' affective vectors the user has watched. We apply five-distance metrics to rank the Top-N movie recommendations against the user's emotion profile. We found Cosine Similarity distance metrics performed better than other distance metrics measures. We conclude that by replacing the top-N recommendations generated by the Recommender with the reranked recommendations list made by the Cosine Similarity distance metrics, the user will effectively get affective aware top-N recommendations while making the Recommender feels like an Emotion Aware Recommender.