Author : Sunil D N 1
Date of Publication :7th August 2016
Abstract: Personalized internet search (PWS) has in contestable its effectiveness in up the standard of varied search services on the net. However, evidences show that users’ reluctance to disclose their personaldatathroughout search has become a significant barrier for the wide proliferation of PWS. we have a tendency to study privacy protection in PWS applications that model user preferences as hierarchic user profiles. we have a tendency to propose a PWS framework known as UPS which will adaptively generalize profiles by queries whereas respecting user specified privacy necessities. Our runtime generalization aims at hanging a balance between 2prophetic metrics that judge the utility of personalization and also the privacy risk of exposing the normalized profile. we have a tendency togift2 greedy algorithms, specifically Greedy DP and Greedy IL, for runtime generalization. we have a tendency toadditionallygivean internet prediction mechanism for deciding whether or not personalizing a question is helpful. intensive experiments demonstrate the effectiveness of our framework. The experimental results ditionally reveal that GreedyILconsiderably outperforms Greedy in terms of potency.
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