TY - GEN
T1 - A hybrid item-based recommendation ranking algorithm based on user access patterns
AU - Hu, Shao Chieh
AU - Yang, Cheng Yi
AU - Liu, Chien Tsai
PY - 2012
Y1 - 2012
N2 - Nowadays, most websites provide tremendous information organized in complex structures of web pages. Therefore, how to help users quickly find pages they are looking for is an important issue. Although a sitemap can provide navigation information across sections of the website, it is static and can hardly provide dynamic information based on access patterns and browsing trends. In this paper, we proposed a hybrid approach for improving recommendation ranking of the web pages for the next visit. Our raking strategy considers not only the relevance (correlation to the next page calculated by the collaborative filtering algorithm) but also the level of interest (time spent on a page) and accessibility (the distance to the next page). In order to evaluate the proposed recommendation ranking algorithm, we used the web access log (IIS log) of a website, Health 99, operated by the Bureau of Health Promotion, Taiwan. The log data was divided into training and testing sets. The measurements of the relevance, the level of interest and the distance factor were computed from the training set. The experimental results showed that the possibility of the pages in the recommendation ranking lists by our approach that were accepted by users was much higher than that proposed by the original collaborative filtering algorithm, particular in short recommendation list (<5).
AB - Nowadays, most websites provide tremendous information organized in complex structures of web pages. Therefore, how to help users quickly find pages they are looking for is an important issue. Although a sitemap can provide navigation information across sections of the website, it is static and can hardly provide dynamic information based on access patterns and browsing trends. In this paper, we proposed a hybrid approach for improving recommendation ranking of the web pages for the next visit. Our raking strategy considers not only the relevance (correlation to the next page calculated by the collaborative filtering algorithm) but also the level of interest (time spent on a page) and accessibility (the distance to the next page). In order to evaluate the proposed recommendation ranking algorithm, we used the web access log (IIS log) of a website, Health 99, operated by the Bureau of Health Promotion, Taiwan. The log data was divided into training and testing sets. The measurements of the relevance, the level of interest and the distance factor were computed from the training set. The experimental results showed that the possibility of the pages in the recommendation ranking lists by our approach that were accepted by users was much higher than that proposed by the original collaborative filtering algorithm, particular in short recommendation list (<5).
KW - Accessibility
KW - Collaborative filtering
KW - IIS log
KW - Ranking
KW - User Access Pattern
UR - http://www.scopus.com/inward/record.url?scp=84865600220&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865600220&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-29458-7_35
DO - 10.1007/978-3-642-29458-7_35
M3 - Conference contribution
AN - SCOPUS:84865600220
SN - 9783642294570
T3 - Advances in Intelligent and Soft Computing
SP - 225
EP - 233
BT - Advanced Technology in Teaching - Selected Papers from the 2012 International Conference on Teaching and Computational Science, ICTCS 2012
T2 - 2012 International Conference on Teaching and Computational Science, ICTCS 2012
Y2 - 1 April 2012 through 2 April 2012
ER -