Recommendation Engine Formation Using Depth First Search and Genetic Approach

Abstract

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 self-adaptive 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.