Abstract:
Today, Web becomes a large repository for
knowledge discovery. However, how to find needed
and related information from the Web is a big
challenge for users. As a solution, Web
personalization and recommendation technique have
evolved. Web recommendation is considered as a
process of identifying user’s preference and adapting
service to satisfy user’s need based on referring the
historical behavior of current user or others who
share similar interest to this user. This paper
describes how to develop a web recommendation
system using Web Usage Mining. Firstly, e-library
system is developed not only for recording log file
but also for evaluating the effectiveness of
recommendation system. Then the explanation of web
log data preprocessing is followed. Secondly, how to
discover the usage patterns from Web log files using
Direct Hashing and Pruning (DHP) association rule
mining algorithm is described. Finally, rule
generation processes based on user input of
threshold values is explained. These rules are used to
provide co-occurrence pages as recommendation to
user.
