PIIR: Proactive Personalized Information Integration and Retrieval


Getting information when needed is the one of the most desirable things for people. Traditional Information Retrieval (IR) systems such as Google and Yahoo have gained wide popularity among the average user by letting them access information using a query. However, a user often does not issue a query when she has an information need, especially when she does not know exactly what needs to be known. On the other hand, the explosion of digital information makes it important to locate the information easily across the user's various dynamic information resources such as the internet, intra net, desktop, laptop, pocketPC, smart phone, and smart wallet.

The proposed research breaks the limitation of existing information accessing methods by focusing on a new information seeking paradigm called Proactive Personalized Information Integration and Retrieval. It differs from traditional search in one major aspect: proactivity. A proactive retrieval agent acts in anticipation of the information needs of the user and recommends information to the user without requiring the user to make an explicit query. To do this, the agent adaptively learns a complex user model while observing and interacting with the user and proactively recommends information to the user without being intrusive.

The project tackles the challenges in developing the agent based on a unified theoretical framework, Bayesian Graphical Models. In particular, the project includes research to:

(1) Learn the optimization goal of the proactive agent as a user-specific, multi-attribute utility function that approximates the user criteria beyond relevance;

(2) Learn user model as a probabilistic graphical model that integrates multiple forms of information such as the context of the user;

(3) Adaptively learn the user model with explicit and implicit feedback from the user as well as other users using Bayesian inference

(4) Proactively recommend documents or queries to the user to optimize the multi-attribute utility based on Bayesian decision theory. Together, these four integrated research thrusts provide a solid foundation for building a proactive personalized search agent.

This web site will be used to disseminate resulting publications, open-source code and annotated test data sets to broad communities for researchers, educators, students and industry practitioners.

This material is based upon work supported by the National Science Foundation under Grant No. IIS-0713111. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

Duration: 08/01/2007-07/31/2011

Last updated: 10/29/2011


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