Adaptive User Profile Model and Collaborative Filtering for Personalized News Export

@incollection{citeulike:6017710, abstract = {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.}, author = {Wang, Jue and Li, Zhiwei and Yao, Jinyi and Sun, Zengqi and Li, Mingjing and Ma, Wei-ying}, citeulike-article-id = {6017710}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11610113_42}, citeulike-linkout-1 = {http://www.springerlink.com/content/p0614603j8g10p85}, doi = {10.1007/11610113_42}, journal = {Frontiers of WWW Research and Development - APWeb 2006}, keywords = {adaptive, collaborative, filtering, news, personalization, user, usermodel, userprofile}, pages = {474--485}, posted-at = {2009-10-28 08:41:10}, priority = {2}, title = {Adaptive User Profile Model and Collaborative Filtering for Personalized News}, url = {http://dx.doi.org/10.1007/11610113_42}, year = {2006} }

TY - CHAP ID - citeulike:6017710 L3 - citeulike-article-id:6017710 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. JF - Frontiers of WWW Research and Development - APWeb 2006 EP - 485 TI - Adaptive User Profile Model and Collaborative Filtering for Personalized News SP - 474 KW - adaptive KW - collaborative KW - filtering KW - news KW - personalization KW - user KW - usermodel KW - userprofile AU - Wang, Jue AU - Li, Zhiwei AU - Yao, Jinyi AU - Sun, Zengqi AU - Li, Mingjing AU - Ma, Wei-ying PY - 2006/// UR - http://dx.doi.org/10.1007/11610113_42 ER -