Audio-Visual Encoding of Multimedia Content to Enhance Movie RecommendationS

Published in Proceedings of the 12th ACM Conference on Recommender Systems, 2018

Recommended citation: Yashar Deldjoo, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Markus Schedl, Bogdan Ionescu, Paolo Cremonesi Proceedings of 12th ACM Conference of Recommender Systems 2018 (RecSys 2018).

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Abstract

We propose a multi-modal content-based movie recommender system that replaces human-generated metadata by content descriptions automatically extracted from the visual and audio channels of a video. Content descriptors improve over traditional metadata in terms of both richness (it is possible to extract hundreds of meaningful features covering various modalities) and quality (content features are consistent across different systems and immune to human errors). Our recommender system integrates state-of-the-art aesthetic and deep visual features as well as block-level and i-vector audio features. For fusing the different modalities, we propose a rank aggregation strategy extending the Borda count approach. We evaluate the proposed multi-modal recommender system comprehensively against metadata-based baselines. To this end, we conduct two empirical studies: (i) a system-centric study to measure the offline quality of recommendations in terms of accuracy-related and beyond-accuracy performance measures (novelty, diversity, and coverage), and (ii) a user-centric online experiment, measuring different subjective metrics, including relevance, satisfaction, and diversity. In both studies, we use a dataset of more than 4,000 movie trailers, which makes our approach versatile. Our results shed light on the accuracy and beyond-accuracy performance of audio, visual, and textual features in content-based movie recommender systems.