Web Page Personalization and link prediction using generalized inverted index and flame clustering

The paper proposes a new scheme named as WPP (Web Page Personalization) for effective web page recommendations. WPP consist of page hit count, total time spent in every link, number of downloads and link separation. Based on these parameters the personalization has been proposed. The system proposes a new implicit user feedback and event link access schemes for effective web page customization along with domain ontology. The proposed system builds a generalized inverted index framework for fast result finding and recommendation. The major goal of this paper is an approach of personalizing users' searches for achieve better search. To get a better item relevance estimation, the system uses the following parameters. (1). Event monitoring and user's click behaviors from Web search, (2). FLAME clustering.