TY - GEN
T1 - Inferring user interests from relevance feedback with high similarity sequence data-driven clustering
AU - Shtykh, Roman Y.
AU - Jin, Qun
PY - 2008/12/1
Y1 - 2008/12/1
N2 - Relevance feedback is an important source of information about a user and often used for usage and user modeling for further personalization of usersystem interactions. In this paper we present a method to infer the user's interests from his/her relevance feedback using an online incremental clustering method. For inference of a new interest (concept) and concept update the method uses the similarity characteristics of uniform user relevance feedback. It is fast, easy to implement and gives reasonable clustering results. We evaluate the method against two different data sets, demonstrate and discuss the outcomes.
AB - Relevance feedback is an important source of information about a user and often used for usage and user modeling for further personalization of usersystem interactions. In this paper we present a method to infer the user's interests from his/her relevance feedback using an online incremental clustering method. For inference of a new interest (concept) and concept update the method uses the similarity characteristics of uniform user relevance feedback. It is fast, easy to implement and gives reasonable clustering results. We evaluate the method against two different data sets, demonstrate and discuss the outcomes.
UR - http://www.scopus.com/inward/record.url?scp=60649092713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=60649092713&partnerID=8YFLogxK
U2 - 10.1109/ISUC.2008.39
DO - 10.1109/ISUC.2008.39
M3 - Conference contribution
AN - SCOPUS:60649092713
SN - 9780769534336
T3 - Proceedings of the 2nd International Symposium on Universal Communication, ISUC 2008
SP - 390
EP - 396
BT - Proceedings of the 2nd International Symposium on Universal Communication, ISUC 2008
T2 - 2nd International Symposium on Universal Communication, ISUC 2008
Y2 - 15 December 2008 through 16 December 2008
ER -