Movie Genome: Alleviating New Item Cold Start in Movie Recommendation
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As of today,mostmovie recommendation services base their recommendations on collaborative
filtering (CF) and/or content-based filtering (CBF)models that usemetadata
(e.g., genre or cast). In most video-on-demand and streaming services, however, new
movies and TV series are continuously added. CF models are unable to make predictions
in such a scenario, since the newly added videos lack interactions?a problem
technically known as new item cold start (CS). Currently, the most common approach
to this problem is to switch to a purely CBF method, usually by exploiting textual metadata.
This approach is known to have lower accuracy than CF because it ignores useful
collaborative information and relies on human-generated textual metadata, which are
expensive to collect and often prone to errors. User-generated content, such as tags,
can also be rare or absent in CS situations. In this paper, we introduce a new movie
recommender system that addresses the new item problem in the movie domain by
(i) integrating state-of-the-art audio and visual descriptors, which can be automatically
extracted from video content and constitute what we call the movie genome;
(ii) exploiting an effective data fusion method named canonical correlation analysis,
which was successfully tested in our previous works Deldjoo et al. (in: International
Conference on Electronic Commerce and Web Technologies. Springer, Berlin, pp
34?45, 2016b; Proceedings of the Twelfth ACM Conference on Recommender Systems.
ACM, 2018b), to better exploit complementary information between different
modalities; (iii) proposing a two-step hybrid approach which trains a CF model on
warm items (items with interactions) and leverages the learned model on the movie
genome to recommend cold items (items without interactions).