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Bhaskar Mehta

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2 papers

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AAAI Conference 2007 Conference Paper

Unsupervised Shilling Detection for Collaborative Filtering

Bhaskar Mehta

Collaborative Filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation. Lies and Propaganda may be spread by malicious users who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, malicious shilling user proﬁles can be injected into a collaborative recommender system which can signiﬁcantly affect the robustness of a recommender system. While current detection algorithms are able to use certain characteristics of shilling proﬁles to detect them, they suffer from low precision, and require a large amount of training data. The aim of this work is to explore simpler unsupervised alternatives which exploit the nature of shilling proﬁles, and can be easily plugged into collaborative ﬁltering framework to add robustness. Two statistical methods are developed and experimentally shown to provide high accuracy in shilling attack detection.

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AAAI Conference 2006 Short Paper

Cross System Personalization by Learning Manifold Alignments

Bhaskar Mehta

Today, personalization in digital libraries and other information systems occurs separately within each system that one interacts with. However, there are several potential improvements w.r.t. such isolated approaches. Investments of users in personalizing a system, either through explicit provision of information, or through long and regular use are not transferable to other systems. Moreover, users have little or no control over the information that defines their profile, since user profiles are deeply buried in personalization engines. Cross-system personalization, i.e. personalization that shares personalization information across different systems in a user-centric way, overcomes the aforementioned problems. Information about users, which is originally scattered across multiple systems, is combined to obtain maximum leverage. The key idea is that when a large number of users cross over from one system to another, carrying their user profiles with them, a mapping between the user profiles of the two systems can be discovered. In this work, we discuss the use of manifold learning for the purpose of computing recomendations for a new user crossing over from one system to another.

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