چکیده (انگلیسی):

The requirement of online users in the website varies
dynamically. The recommendation of web pages consisting of user
expected information and data is performed by the online
recommendation system. The recommendation engine must be selfadaptive
and accurate. The existing algorithm uses Depth First Search
(DFS) and bee’s foraging approach to create navigation profiles by
categorizing the current user activity. The prediction of navigations
that are most expected to be visited by online users is also performed.
In this study, the recommendation engine formation with optimized
resource such as memory, CPU usage and minimum time consumption is
proposed using DFS and Genetic Approach (GA). Here, initially the cluster
formation is achieved using DFS approach. The method creates an eminent
browsing pattern for each user using live session window. The performance
of the approach is compared with the existing forager agent. The
experimental results show that the proposed approach outperforms the
existing methods in accomplishing accurate classification and anticipation of
future navigation for the current online user.