Personalizing Search Based on user Search Histories (original) (raw)
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Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidences show that users' reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS. We study privacy protection in PWS applications that model user preferences as hierarchical user profiles. We propose a PWS framework called UPS that can adaptively generalize profiles by queries while respecting userspecified privacy requirements.
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IMPLEMENTING THE SUPPORTING PRIVACY PROTECTION IN CUSTOMIZED NET SEARCH
Personalized internet search (PWS) has in contestible its effectiveness in up the standard of varied search services on the net. However, evidences show that users’ reluctance to disclose their personal data throughout 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 2 prophetic metrics that judge the utility of personalization and also the privacy risk of exposing the unrealized profile. We have a tendency to gift 2 greedy algorithms, specifically GreedyDP and GreedyIL, for runtime generalization. We have a tendency to additionally give an internet prediction mechanism for deciding whether or not personalizing a question is helpful. Intensive experiments demonstrate the effectiveness of our framework. The experimental results additionally reveal that GreedyIL considerably outperforms GreedyDP in terms of potency. Index Terms‐ Privacy protection, personalized web search, utility, risk, profile
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Personalized Web Search Protecting Privacy Using Greedy Algorithm
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The amount of information on the World Wide Web is growing rapidly; search engines must be able to retrieve information according to the user's preference. Current web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of web search is to carry out retrieval for each user incorporating his/her interests. Every user has a distinct background and a specific goal when searching for information on the Web. Thus the goal of Web search personalization is to tailor search results to a particular user based on that user’s interests and preferences. However, effective personalized search requires collecting and aggregating user information, which often raises serious concerns of privacy infringement for many users. Indeed, these concerns have become one of the main barriers for deploying personalized search applications, and how to do privacy-preserving personalization is a great challenge. Thus, a balance must be struck ...
SpringerReference
We study the problem of anonymizing user profiles so that user privacy is sufficiently protected while the anonymized profiles are still effective in enabling personalized web search. We propose a Bayes-optimal privacy based principle to bound the prior and posterior probability of associating a user with an individual term in the anonymized user profile set. We also propose a novel bundling technique that clusters user profiles into groups by taking into account the semantic relationships between the terms while satisfying the privacy constraint. We evaluate our approach through a set of preliminary experiments using real data demonstrating its feasibility and effectiveness.