TY - GEN
T1 - Personalized online document, image and video recommendation via commodity eye-tracking
AU - Xu, Songhua
AU - Jiang, Hao
AU - Lau, Francis C.M.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Commodity eye-tracking
KW - Document
KW - Image and video recommendation
KW - Implicit user feedback
KW - Personalized recommendation and ranking
KW - User attention
KW - Web search
UR - http://www.scopus.com/inward/record.url?scp=63449112393&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=63449112393&partnerID=8YFLogxK
U2 - 10.1145/1454008.1454023
DO - 10.1145/1454008.1454023
M3 - Conference contribution
AN - SCOPUS:63449112393
SN - 9781605580937
T3 - RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems
SP - 83
EP - 90
BT - RecSys'08
T2 - 2008 2nd ACM International Conference on Recommender Systems, RecSys'08
Y2 - 23 October 2008 through 25 October 2008
ER -