Rutgers Filtering Work at TREC 2002: Adaptive and Batch

Anghelescu, Andrei; Boros, Endre; Lewis, David; Menkov, Vladimir; Neu, David J.; Kantor, Paul B.

doi:10.21236/ada459191

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…It was created as a subfield of the more general information retrieval field, which was originally established to solve the ad hoc retrieval task. 5 For this reason, early work tended to view filtering and retrieval as "two sides of the same coin" [14]. The duality argument is based on the assumptions that documents and queries are interchangeable.…”

Section: Existing Retrieval Modelsmentioning

confidence: 99%

Adaptive Information Filtering

2009

Text Mining

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Section: Existing Retrieval Modelsmentioning

confidence: 99%

Adaptive Information Filtering

2009

Text Mining

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“…The two systems that we chose to interpolate between are DIMACS Rocchio [2] (TREC run dimacs11aAPQ) and the Rocchio system constructed by Chinese Academy of Science (CAS) [16] (TREC run ICTAdaFT11Ua). A Rocchio model uses the original query text, the known relevant examples, and information about the centroids of the vectors representing the set of judged relevant documents and the set of judged but not relevant documents.…”

Section: Trec-11mentioning

confidence: 99%

A design space approach to analysis of information retrieval adaptive filtering systems

Fradkin

Kantor

2004

Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management

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In this paper we suggest a new approach to analysis and design of IR systems. We argue for design space exploration in constructing IR systems and in analyzing the effects of individual modules and parameters. We present results of experiments with parametric interpolation, or " homotopy", between two systems, and show, incidentally, that the best results are not achieved at the endpoints, and may lie outside the bounding hypercube defined by our choice of parameterization. Three distinct classes of interpolation are introduced to deal with the complexities of the specific example.

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Bayesian graphical models for adaptive filtering

Zhang

2005

SIGIR Forum

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A personal information filtering system monitors an incoming document stream to find the documents that match information needs specified by user profiles. The most challenging aspect in adaptive filtering is to develop a system to learn user profiles efficiently and effectively from very limited user supervision.In order to overcome this challenge, the system needs to do the following: use a robust learning algorithm that can work reasonably well when the amount of training data is small and be more effective with more training data; explore what a user likes while satisfying the user's immediate information need and trade off exploration and exploitation; consider many aspects of a document besides relevance, such as novelty, readability and authority; use multiple forms of evidence, such as user context and implicit feedback from the user, while interacting with a user; and handle various scenarios, such as missing data, in an operational environment robustly.This dissertation uses the Bayesian graphical modelling approach as a unified framework for filtering. We customize the framework to the filtering domain and develop a set of solutions that enable us to build a filtering system with the desired characteristics in a principled way. We evaluate and justify these solutions on a large and diverse set of standard and new adaptive filtering test collections. Firstly, we develop a novel technique to incorporate an IR expert's heuristic algorithm as a Bayesian prior into a machine learning classifier to improve the robustness of a filtering system. Secondly, we derive a novel model quality measure based on the uncertainty of model parameters to trade off exploration and exploitation and do active learning. Thirdly, we carry out a user study with a real web-based personal news filtering system and more than 20 users. With the data collected in the user study, we explore how to use existing graphical modeling algorithms to learn the causal relationships between multiple forms of evidence and improve the filtering system's performance using this evidence.A copy of the dissertation is available at

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Rutgers Filtering Work at TREC 2002: Adaptive and Batch

Cited by 3 publications

References 4 publications

Adaptive Information Filtering

Adaptive Information Filtering

A design space approach to analysis of information retrieval adaptive filtering systems

Bayesian graphical models for adaptive filtering

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