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Personalized Web Search using Temporal Behaviour over the Re-Ranking With High Speed Memory

Author(s):

N. Hamsaleka , GOBI ARTS & SCIENCE COLLEGE; P. Elango, GOBI ARTS & SCIENCE COLLEGE

Keywords:

Personalized Web Search, Proliferation, Predictive Metrics, Greedy Information Loss, Cross Re-Ranking Algorithm

Abstract

Personalized internet search (PWS) has demonstrated its effectiveness in improving the standard of various search services on the internet. However, evidences show that users’ reluctance to disclose their personal data throughout search has become a serious barrier for the wide proliferation of PWS. In this paper studies privacy protection in PWS applications that model user preferences as hierarchical user profiles. In this paper proposes a PWS framework known as UPS that can adaptively generalize profiles by queries while respecting user-specified privacy necessities. The proposed runtime generalization aims at placing a balance between two predictive metrics that valuate the utility of personalization and the privacy risk of exposing the generalized profile. In this paper presents a greedy algorithmic program, specifically GreedyIL, for runtime generalization. It also provides an online prediction mechanism for deciding whether or not personalizing a query is helpful exploitation cross re-ranking algorithmic program with catch method.

Other Details

Paper ID: IJSRDV3I60408
Published in: Volume : 3, Issue : 6
Publication Date: 01/09/2015
Page(s): 785-788

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