TY - GEN
T1 - Recommending web pages using item-based collaborative filtering approaches
AU - Cadegnani, Sara
AU - Guerra, Francesco
AU - Ilarri, Sergio
AU - Rodríguez-Hernández, María del Carmen
AU - Trillo-Lado, Raquel
AU - Velegrakis, Yannis
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Predicting the next page a user wants to see in a large website has gained importance along the last decade due to the fact that the Web has become the main communication media between a wide set of entities and users. This is true in particular for institutional government and public organization websites, where for transparency reasons a lot of information has to be provided. The “long tail” phenomenon affects also this kind of websites and users need support for improving the effectiveness of their navigation. For this reason, complex models and approaches for recommending web pages that usually require to process personal user preferences have been proposed. In this paper, we propose three different approaches to leverage information embedded in the structure of web sites and their logs to improve the effectiveness of web page recommendation by considering the context of the users, i.e., their current sessions when surfing a specific web site. This proposal does not require either information about the personal preferences of the users to be stored and processed or complex structures to be created and maintained. So, it can be easily incorporated to current large websites to facilitate the users’ navigation experience. Experiments using a real-world website are described and analyzed to show the performance of the three approaches.
AB - Predicting the next page a user wants to see in a large website has gained importance along the last decade due to the fact that the Web has become the main communication media between a wide set of entities and users. This is true in particular for institutional government and public organization websites, where for transparency reasons a lot of information has to be provided. The “long tail” phenomenon affects also this kind of websites and users need support for improving the effectiveness of their navigation. For this reason, complex models and approaches for recommending web pages that usually require to process personal user preferences have been proposed. In this paper, we propose three different approaches to leverage information embedded in the structure of web sites and their logs to improve the effectiveness of web page recommendation by considering the context of the users, i.e., their current sessions when surfing a specific web site. This proposal does not require either information about the personal preferences of the users to be stored and processed or complex structures to be created and maintained. So, it can be easily incorporated to current large websites to facilitate the users’ navigation experience. Experiments using a real-world website are described and analyzed to show the performance of the three approaches.
UR - http://www.scopus.com/inward/record.url?scp=84955245612&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27932-9_2
DO - 10.1007/978-3-319-27932-9_2
M3 - Conference contribution
AN - SCOPUS:84955245612
SN - 9783319279312
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 17
EP - 29
BT - Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers
A2 - Velegrakis, Yannis
A2 - Cardoso, Jorge
A2 - Cardoso, Jorge
A2 - Pinto, Alexandre Miguel
A2 - Guerra, Francesco
A2 - Houben, Geert-Jan
PB - Springer
T2 - 1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015
Y2 - 8 September 2015 through 9 September 2015
ER -