TY - GEN
T1 - User-centric integrated recommendation by gradual adaptation based on focus of interests and its transition
AU - Chen, Jian
AU - Zhou, Xiaokang
AU - Jin, Qun
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Recommender system is a focus in the age of information explosion. In this study, with the benefit of social networking service, we propose a User-Centric Integrated Recommendation Model based on combining of users' individualities and commonalities, in which users' interests are focused and their transitions are traced by analyzing users' information access behaviors and histories, and then a sequence of information seeking actions are recommended to target users through dectecting the transitions of their interests focus by interaction of users and the system, and extracting successful experience from a reference user group, in which the reference users are similar to the target users. A set of bookmark tags are used to describe relations of Web pages. The pages accessed by users are classified by the bookmark tags, and grouped into two categories of individual and common interests and their sub-categories. The individual interests are divided into three types: strong interest, weak interest and uncertain interest. The common interests are divided into popular interest, public interest and private interest. In this paper, in addition to describing definitions and measures, we present a mechanism of inferring interest focus and show the system architecture. Finally, the conclusion and further work are introduced.
AB - Recommender system is a focus in the age of information explosion. In this study, with the benefit of social networking service, we propose a User-Centric Integrated Recommendation Model based on combining of users' individualities and commonalities, in which users' interests are focused and their transitions are traced by analyzing users' information access behaviors and histories, and then a sequence of information seeking actions are recommended to target users through dectecting the transitions of their interests focus by interaction of users and the system, and extracting successful experience from a reference user group, in which the reference users are similar to the target users. A set of bookmark tags are used to describe relations of Web pages. The pages accessed by users are classified by the bookmark tags, and grouped into two categories of individual and common interests and their sub-categories. The individual interests are divided into three types: strong interest, weak interest and uncertain interest. The common interests are divided into popular interest, public interest and private interest. In this paper, in addition to describing definitions and measures, we present a mechanism of inferring interest focus and show the system architecture. Finally, the conclusion and further work are introduced.
KW - data mining
KW - gradual adaptation
KW - information recommendation
KW - user-centric
UR - http://www.scopus.com/inward/record.url?scp=84872282044&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872282044&partnerID=8YFLogxK
U2 - 10.1109/CIT.2012.101
DO - 10.1109/CIT.2012.101
M3 - Conference contribution
AN - SCOPUS:84872282044
SN - 9780769548586
T3 - Proceedings - 2012 IEEE 12th International Conference on Computer and Information Technology, CIT 2012
SP - 435
EP - 441
BT - Proceedings - 2012 IEEE 12th International Conference on Computer and Information Technology, CIT 2012
T2 - 2012 IEEE 12th International Conference on Computer and Information Technology, CIT 2012
Y2 - 27 October 2012 through 29 October 2012
ER -