  In this paper we examine the ability of low-level multimodal features to
extract movie similarity, in the context of a content-based movie
recommendation approach. In particular, we demonstrate the extraction of
multimodal representation models of movies, based on textual information from
subtitles, as well as cues from the audio and visual channels. With regards to
the textual domain, we emphasize our research in topic modeling of movies based
on their subtitles, in order to extract topics that discriminate between
movies. Regarding the visual domain, we focus on the extraction of semantically
useful features that model camera movements, colors and faces, while for the
audio domain we adopt simple classification aggregates based on pretrained
models. The three domains are combined with static metadata (e.g. directors,
actors) to prove that the content-based movie similarity procedure can be
enhanced with low-level multimodal information. 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
individual modalities and fusion models, in the form of recommendation
rankings. Extensive experimentation proves that all three low-level modalities
(text, audio and visual) boost the performance of a content-based
recommendation system, compared to the typical metadata-based content
representation, by more than $50\%$ relative increase. To our knowledge, this
is the first approach that utilizes a wide range of features from all involved
modalities, in order to enhance the performance of the content similarity
estimation, compared to the metadata-based approaches.
