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

Ioannis Arapakis, Yashar Moshfeghi, Hideo Joho, Reede Ren, David Hannah, Joemon M. Jose

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

29 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the ACM International Conference on Image and Video Retrieval
Place of PublicationNew York
Number of pages8
DOIs
Publication statusPublished - 8 Jul 2009
EventACM International Conference on Image and Video Retrieval 2009 - Santorini, Greece
Duration: 8 Jul 200910 Jul 2009

Conference

ConferenceACM International Conference on Image and Video Retrieval 2009
Abbreviated titleCIVR 2009
CountryGreece
CitySantorini
Period8/07/0910/07/09

Keywords

  • recommender systems
  • video search interface
  • interaction data
  • affective responses
  • contextual information

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