TY - GEN
T1 - Ranking web search results from personalized perspective
AU - Peng, Wen-Chih
AU - Lin, Yu Chin
PY - 2006/12/1
Y1 - 2006/12/1
N2 - In this paper, we exploit the technique of data mining to mine frequent access patterns from user browsing behavior. In light of frequent access patterns, we develop a solution procedure to automatically extract user interests. Furthermore, in accordance with user interests mined and feedbacks of users, we propose a new algorithm with the idea of dynamically adjusting the ranking scores of Web pages. Specifically, algorithm PPR (standing for Personalized PageRank), is divided into four phases. The first phase assigns the initial weights based on user interests. In the second phase, the virtual links and hubs are created according to user interests. By observing user click streams, our proposed algorithm will incrementally reflect user favors for the personalized ranking in the third phase. To improve the accuracy of ranking, collaborative filtering is taken into consideration when the new query is submitted. By conducting simulation experiments, we have shown that algorithm PPR is not only very effective but also very adaptive in providing personalized ranking to users.
AB - In this paper, we exploit the technique of data mining to mine frequent access patterns from user browsing behavior. In light of frequent access patterns, we develop a solution procedure to automatically extract user interests. Furthermore, in accordance with user interests mined and feedbacks of users, we propose a new algorithm with the idea of dynamically adjusting the ranking scores of Web pages. Specifically, algorithm PPR (standing for Personalized PageRank), is divided into four phases. The first phase assigns the initial weights based on user interests. In the second phase, the virtual links and hubs are created according to user interests. By observing user click streams, our proposed algorithm will incrementally reflect user favors for the personalized ranking in the third phase. To improve the accuracy of ranking, collaborative filtering is taken into consideration when the new query is submitted. By conducting simulation experiments, we have shown that algorithm PPR is not only very effective but also very adaptive in providing personalized ranking to users.
KW - Data mining
KW - Personalization
KW - Web mining
UR - http://www.scopus.com/inward/record.url?scp=33845865255&partnerID=8YFLogxK
U2 - 10.1109/CEC-EEE.2006.72
DO - 10.1109/CEC-EEE.2006.72
M3 - Conference contribution
AN - SCOPUS:33845865255
SN - 0769525113
SN - 9780769525112
T3 - CEC/EEE 2006 Joint Conferences
BT - Proceedings - CEC/EEE 2006
Y2 - 26 June 2006 through 29 June 2006
ER -