Multimodal Content Representation and Similarity Ranking of Movies
This work addresses movie recommendation and exploration for users by providing a multimodal similarity system, but it is incremental as it builds on existing topic modeling and fusion techniques.
The paper tackled the problem of correlating movie similarity with low-level multimodal features by extracting representations from subtitles, audio, and metadata, and demonstrated this through a dataset of 160 movies to produce recommendation rankings and a topic model browser.
In this paper we examine the existence of correlation between movie similarity and low level features from respective movie content. In particular, we demonstrate the extraction of multi-modal representation models of movies based on subtitles, audio and metadata mining. We emphasize our research in topic modeling of movies based on their subtitles. In order to demonstrate the proposed content representation approach, we have built a small dataset of 160 widely known movies. We assert movie similarities, as propagated by the singular modalities and fusion models, in the form of recommendation rankings. We showcase a novel topic model browser for movies that allows for exploration of the different aspects of similarities between movies and an information retrieval system for movie similarity based on multi-modal content.