TY - GEN
T1 - Adaptive user profile model and collaborative filtering for personalized news
AU - Wang, Jue
AU - Li, Zhiwei
AU - Yao, Jinyi
AU - Sun, Zengqi
AU - Li, Mingjing
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - In recent years, personalized news recommendation has received increasing attention in IR community. The core problem of personalized recommendation is to model and track users' interests and their changes. To address this problem, both content-based filtering (CBF) and collaborative filtering (CF) have been explored. User interests involve interests on fixed categories and dynamic events, yet in current CBF approaches, there is a lack of ability to model user's interests at the event level. In this paper, we propose a novel approach to user profile modeling. In this model, user's interests are modeled by a multi-layer tree with a dynamically changeable structure, the top layers of which are used to model user interests on fixed categories, and the bottom layers are for dynamic events. Thus, this model can track the user's reading behaviors on both fixed categories and dynamic events, and consequently capture the interest changes. A modified CF algorithm based on the hierarchically structured profile model is also proposed. Experimental results indicate the advantages of our approach. © Springer-Verlag Berlin Heidelberg 2006.
AB - In recent years, personalized news recommendation has received increasing attention in IR community. The core problem of personalized recommendation is to model and track users' interests and their changes. To address this problem, both content-based filtering (CBF) and collaborative filtering (CF) have been explored. User interests involve interests on fixed categories and dynamic events, yet in current CBF approaches, there is a lack of ability to model user's interests at the event level. In this paper, we propose a novel approach to user profile modeling. In this model, user's interests are modeled by a multi-layer tree with a dynamically changeable structure, the top layers of which are used to model user interests on fixed categories, and the bottom layers are for dynamic events. Thus, this model can track the user's reading behaviors on both fixed categories and dynamic events, and consequently capture the interest changes. A modified CF algorithm based on the hierarchically structured profile model is also proposed. Experimental results indicate the advantages of our approach. © Springer-Verlag Berlin Heidelberg 2006.
UR - https://www.scopus.com/pages/publications/33745667997
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33745667997&origin=recordpage
U2 - 10.1007/11610113_42
DO - 10.1007/11610113_42
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3540311424
SN - 9783540311423
VL - 3841 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 474
EP - 485
BT - Frontiers of WWW Research and Development - APWeb 2006 - 8th Asia-Pacific Web Conference, Proceedings
PB - Springer Verlag
T2 - 8th Asia-Pacific Web Conference, APWeb 2006: Frontiers of WWW Research and Development
Y2 - 16 January 2006 through 18 January 2006
ER -