Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
A hybrid recommendation model for web navigation. / Al-Yazeed, Noaman M.Abo; Gadallah, Ahmed M.; Hefny, Hesham A.
2015 IEEE 7th International Conference on Intelligent Computing and Information Systems, ICICIS 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 552-560 7397276 (2015 IEEE 7th International Conference on Intelligent Computing and Information Systems, ICICIS 2015).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - A hybrid recommendation model for web navigation
AU - Al-Yazeed, Noaman M.Abo
AU - Gadallah, Ahmed M.
AU - Hefny, Hesham A.
PY - 2016/2/2
Y1 - 2016/2/2
N2 - Nowadays, users rely on the web for information gathering. Accordingly, web usage mining becomes one important subject of research. Such research area covers prediction of user near future intentions, web-based personalized services, customer profiling, and adaptive web sites. Web page prediction is strongly limited by the nature of web logs, the intrinsic complexity of the problem and the tight efficiency requirements. This paper proposes a hybrid page ranking model based on web usage mining technique by exploiting session data of users, to enhance the recommendations of the next candidate web page to be accessed. The proposed approach represents a combination between two page ranking approaches. The first one computes the frequency ratio indicating the number of occurrences of each page in the search result. On the other hand, the second approach computes the coverage ratio from similar behavior patterns. As a result of the proposed approach, a set of candidate pages are ranked and the page with highest rate is recommended. The proposed approach has been tested on real data collected and extracted from the web server log file of CTI main web server.
AB - Nowadays, users rely on the web for information gathering. Accordingly, web usage mining becomes one important subject of research. Such research area covers prediction of user near future intentions, web-based personalized services, customer profiling, and adaptive web sites. Web page prediction is strongly limited by the nature of web logs, the intrinsic complexity of the problem and the tight efficiency requirements. This paper proposes a hybrid page ranking model based on web usage mining technique by exploiting session data of users, to enhance the recommendations of the next candidate web page to be accessed. The proposed approach represents a combination between two page ranking approaches. The first one computes the frequency ratio indicating the number of occurrences of each page in the search result. On the other hand, the second approach computes the coverage ratio from similar behavior patterns. As a result of the proposed approach, a set of candidate pages are ranked and the page with highest rate is recommended. The proposed approach has been tested on real data collected and extracted from the web server log file of CTI main web server.
KW - Adaptive Web Sites
KW - Navigation Pattern Mining
KW - Recommender System
KW - Web Log
KW - Web Mining
KW - Web Personalization
KW - Web Usage Mining
KW - Web-Based Recommendation Systems
UR - http://www.scopus.com/inward/record.url?scp=84969931992&partnerID=8YFLogxK
U2 - 10.1109/IntelCIS.2015.7397276
DO - 10.1109/IntelCIS.2015.7397276
M3 - Conference contribution
AN - SCOPUS:84969931992
T3 - 2015 IEEE 7th International Conference on Intelligent Computing and Information Systems, ICICIS 2015
SP - 552
EP - 560
BT - 2015 IEEE 7th International Conference on Intelligent Computing and Information Systems, ICICIS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2015
Y2 - 12 December 2015 through 14 December 2015
ER -
ID: 53045170