Liu, B.; Millett, H.; Rebola, B.L.; Svensen, P. Dictionary Learning for Personalized Multimodal Recommendation. Preprints2022, 2022060412. https://doi.org/10.20944/preprints202206.0412.v1
APA Style
Liu, B., Millett, H., Rebola, B.L., & Svensen, P. (2022). Dictionary Learning for Personalized Multimodal Recommendation. Preprints. https://doi.org/10.20944/preprints202206.0412.v1
Chicago/Turabian Style
Liu, B., Bruno L. Rebola and Plank Svensen. 2022 "Dictionary Learning for Personalized Multimodal Recommendation" Preprints. https://doi.org/10.20944/preprints202206.0412.v1
Abstract
In today’s Web 2.0 era, online social media has become an integral part of our lives. In the course of the informationrevolution, the form of information has undergone a radical change, from simple text information to today’s integrated video, image, textand audio, and there has also been a great change in the way of dissemination and access, as people nowadays do not just rely ontraditional media to passively receive information, but more actively and selectively obtain information from social media. Therefore, ithas become a great challenge for us to effectively utilize these massive and integrated multi-modal media information to form an effectivesystem of retrieval, browsing, analysis and usage. Unlike movies and traditional long-form video content, micro-videos are usually shortin length, between a few seconds and tens of seconds, which allows users to quickly browse different contents and make full use of thefragmented time in their lives, while users can also share their micro-videos to their friends or the public, forming a unique social way.Video contains rich multimodal information, and fusing information from multiple modalities in a video recommendation task can improvethe accuracy of the video recommendation task.According to the micro-video recommendation task, a new combinatorial network modelis proposed to combine the discrete features of each modality into the overall features of various modalities through the network, andthen fuse the various modal features to obtain the overall video features, which will be used for recommendation. In order to verify theeffectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectivenessof our model.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
