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Using visual features based on MPEG-7 and deep learning for movie recommendation

Using visual features based on MPEG-7 and deep learning for movie recommendation Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the crowd (e.g., tag), and as such, they are prone to noise and are expensive to collect. Moreover, these features are often rare or absent for new items, making it difficult or even impossible to provide good quality recommendations. In this paper, we show that users’ preferences on movies can be well or even better described in terms of the mise-en-scène features, i.e., the visual aspects of a movie that characterize design, aesthetics and style (e.g., colors, textures). We use both MPEG-7 visual descriptors and Deep Learning hidden layers as examples of mise-en-scène features that can visually describe movies. These features can be computed automatically from any video file, offering the flexibility in handling new items, avoiding the need for costly and error-prone human-based tagging, and providing good scalability. We have conducted a set of experiments on a large catalog of 4K movies. Results show that recommendations based on mise-en-scène features consistently outperform traditional metadata attributes (e.g., genre and tag). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Using visual features based on MPEG-7 and deep learning for movie recommendation

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Database Management
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-018-0155-1
Publisher site
See Article on Publisher Site

Abstract

Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the crowd (e.g., tag), and as such, they are prone to noise and are expensive to collect. Moreover, these features are often rare or absent for new items, making it difficult or even impossible to provide good quality recommendations. In this paper, we show that users’ preferences on movies can be well or even better described in terms of the mise-en-scène features, i.e., the visual aspects of a movie that characterize design, aesthetics and style (e.g., colors, textures). We use both MPEG-7 visual descriptors and Deep Learning hidden layers as examples of mise-en-scène features that can visually describe movies. These features can be computed automatically from any video file, offering the flexibility in handling new items, avoiding the need for costly and error-prone human-based tagging, and providing good scalability. We have conducted a set of experiments on a large catalog of 4K movies. Results show that recommendations based on mise-en-scène features consistently outperform traditional metadata attributes (e.g., genre and tag).

Journal

International Journal of Multimedia Information RetrievalSpringer Journals

Published: Jun 14, 2018

References