Text-based Emotion Aware Recommender

TL;DR

This paper introduces an emotion-aware recommender system that uses user and movie emotion vectors derived from movie overviews and user history, enhancing recommendation diversity and serendipity.

Contribution

It presents a novel approach combining emotion vectors with content-based and collaborative filtering for improved recommendations.

Findings

Emotion-aware recommender shows increased serendipity.

The system effectively classifies movies' emotions using Tweets Affective Classifier.

Emotion vectors improve recommendation diversity.

Abstract

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.