Enriching user profiling with affective features for the improvement of a multimodal recommender system

Arapakis, I., Moshfeghi, Y., Joho, H., Ren, R., Hannah, D. and Jose, J. (2009) Enriching user profiling with affective features for the improvement of a multimodal recommender system. In: ACM International Conference on Image and Video Retrieval, Santorini, Fira, Greece, 8-10 Jul 2009, ISBN 9781605584805 (doi: 10.1145/1646396.1646433)

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Publisher's URL: http://dx.doi.org/10.1145/1646396.1646433

Abstract

Recommender systems have been systematically applied in industry and academia to help users cope with information uncertainty. However, given the multiplicity of the preferences and needs it has been shown that no approach is suitable for all users in all situations. Thus, it is believed that an effective recommender system should incorporate a variety of techniques and features to offer valuable recommendations and enhance the search experience. In this paper we propose a novel video search interface that employs a multimodal recommender system, which can predict topical relevance. The multimodal recommender accounts for interaction data, contextual information, as well as users' affective responses, and exploits these information channels to provide meaningful recommendations of unseen videos. Our experiment shows that the multimodal interaction feature is a promising way to improve the performance of recommendation.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Ren, Dr R and Hannah, Mr David and Joho, Dr Hideo and Moshfeghi, Dr Yashar and Arapakis, Mr Ioannis
Authors: Arapakis, I., Moshfeghi, Y., Joho, H., Ren, R., Hannah, D., and Jose, J.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781605584805

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