Personalized recommendation driven by information flow Export

@inproceedings{citeulike:989320, abstract = {We propose that the information access behavior of a group of people can be modeled as an information flow issue, in which people intentionally or unintentionally influence and inspire each other, thus creating an interest in retrieving or getting a specific kind of information or product. Information flow models how information is propagated in a social network. It can be a real social network where interactions between people reside; it can be, moreover, a virtual social network in that people only influence each other unintentionally, for instance, through collaborative filtering. We leverage users' access patterns to model information flow and generate effective personalized recommendations. First, an early adoption based information flow (EABIF) network describes the influential relationships between people. Second, based on the fact that adoption is typically category specific, we propose a topic-sensitive EABIF (TEABIF) network, in which access patterns are clustered with respect to the categories. Once an item has been accessed by early adopters, personalized recommendations are achieved by estimating whom the information will be propagated to with high probabilities. In our experiments with an online document recommendation system, the results demonstrate that the EABIF and the TEABIF can respectively achieve an improved (precision, recall) of (91.0\%, 87.1\%) and (108.5\%, 112.8\%) compared to traditional collaborative filtering, given an early adopter exists.}, address = {New York, NY, USA}, author = {Song, Xiaodan and Tseng, Belle L. and Lin, Ching-Yung and Sun, Ming-Ting}, booktitle = {SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval}, citeulike-article-id = {989320}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1148170.1148258}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1148170.1148258}, doi = {10.1145/1148170.1148258}, isbn = {1595933697}, keywords = {collaborative-filtering, informaion-retrieval, social-navigation}, pages = {509--516}, posted-at = {2007-09-08 10:28:20}, priority = {2}, publisher = {ACM Press}, title = {Personalized recommendation driven by information flow}, url = {http://dx.doi.org/10.1145/1148170.1148258}, year = {2006} }

TY - CONF ID - citeulike:989320 L3 - citeulike-article-id:989320 N2 - We propose that the information access behavior of a group of people can be modeled as an information flow issue, in which people intentionally or unintentionally influence and inspire each other, thus creating an interest in retrieving or getting a specific kind of information or product. Information flow models how information is propagated in a social network. It can be a real social network where interactions between people reside; it can be, moreover, a virtual social network in that people only influence each other unintentionally, for instance, through collaborative filtering. We leverage users' access patterns to model information flow and generate effective personalized recommendations. First, an early adoption based information flow (EABIF) network describes the influential relationships between people. Second, based on the fact that adoption is typically category specific, we propose a topic-sensitive EABIF (TEABIF) network, in which access patterns are clustered with respect to the categories. Once an item has been accessed by early adopters, personalized recommendations are achieved by estimating whom the information will be propagated to with high probabilities. In our experiments with an online document recommendation system, the results demonstrate that the EABIF and the TEABIF can respectively achieve an improved (precision, recall) of (91.0%, 87.1%) and (108.5%, 112.8%) compared to traditional collaborative filtering, given an early adopter exists. SN - 1595933697 AD - New York, NY, USA EP - 516 TI - Personalized recommendation driven by information flow PB - ACM Press T2 - SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval SP - 509 KW - collaborative-filtering KW - informaion-retrieval KW - social-navigation AU - Song, Xiaodan AU - Tseng, Belle AU - Lin, Ching-Yung AU - Sun, Ming-Ting PY - 2006/// UR - http://dx.doi.org/10.1145/1148170.1148258 ER -