TY - GEN
T1 - Usage-based PageRank for Web personalization
AU - Eirinaki, Magdalini
AU - Vazirgiannis, Michalis
PY - 2005/1/1
Y1 - 2005/1/1
N2 - Recommendation algorithms aim at proposing "next" pages to a user based on her current visit and the past users ' navigational patterns. In the vast majority of related algorithms, only the usage data are used to produce recommendations, whereas the structural properties of the Web graph are ignored. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this paper we present UPR, a novel personalization algorithm which combines usage data and link analysis techniques for ranking and recommending web pages to the end user. Using the web site's structure and its usage data we produce personalized navigational graph synopses (prNG) to be used for applying UPR and produce personalized recommendations. Experimental results show that the accuracy of the recommendations is superior to pure usage-based approaches.
AB - Recommendation algorithms aim at proposing "next" pages to a user based on her current visit and the past users ' navigational patterns. In the vast majority of related algorithms, only the usage data are used to produce recommendations, whereas the structural properties of the Web graph are ignored. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this paper we present UPR, a novel personalization algorithm which combines usage data and link analysis techniques for ranking and recommending web pages to the end user. Using the web site's structure and its usage data we produce personalized navigational graph synopses (prNG) to be used for applying UPR and produce personalized recommendations. Experimental results show that the accuracy of the recommendations is superior to pure usage-based approaches.
U2 - 10.1109/ICDM.2005.148
DO - 10.1109/ICDM.2005.148
M3 - Conference contribution
AN - SCOPUS:34250726753
SN - 0769522785
SN - 9780769522784
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 130
EP - 137
BT - Proceedings - 5th IEEE International Conference on Data Mining, ICDM 2005
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Data Mining, ICDM 2005
Y2 - 27 November 2005 through 30 November 2005
ER -