Personalized online document, image and video recommendation via commodity eye-tracking

Songhua Xu, Hao Jiang, Francis C.M. Lau

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

69 Scopus citations

Abstract

We propose a new recommendation algorithm for online documents, images and videos, which is personalized. Our idea is to rely on the attention time of individual users captured through commodity eye-tracking as the essential clue. The prediction of user interest over a certain online item (a document, image or video) is based on the user's attention time acquired using vision-based commodity eye-tracking during his previous reading, browsing or video watching sessions over the same type of online materials. After acquiring a user's attention times over a collection of online materials, our algorithm can predict the user's probable attention time over a new online item through data mining. Based on our proposed algorithm, we have developed a new online content recommender system for documents, images and videos. The recommendation results produced by our algorithm are evaluated by comparing with those manually labeled by users as well as by commercial search engines including Google (Web) Search, Google Image Search and YouTube.

Original languageEnglish (US)
Title of host publicationRecSys'08
Subtitle of host publicationProceedings of the 2008 ACM Conference on Recommender Systems
Pages83-90
Number of pages8
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 2nd ACM International Conference on Recommender Systems, RecSys'08 - Lausanne, Switzerland
Duration: Oct 23 2008Oct 25 2008

Publication series

NameRecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems

Other

Other2008 2nd ACM International Conference on Recommender Systems, RecSys'08
Country/TerritorySwitzerland
CityLausanne
Period10/23/0810/25/08

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Control and Systems Engineering

Keywords

  • Commodity eye-tracking
  • Document
  • Image and video recommendation
  • Implicit user feedback
  • Personalized recommendation and ranking
  • User attention
  • Web search

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