Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719071
Title: The relevance of feedback for text retrieval
Author: Vinay, V.
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2007
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Abstract:
Relevance Feedback is a technique that helps an Information Retrieval system modify a query in response to relevance judgements provided by the user about individual results dis played after an initial retrieval. This thesis begins by proposing an evaluation framework for measuring the effectiveness of feedback algorithms. The simulation-based method in volves a brute force exploration of the outcome of every possible user action. Starting from an initial state, each available alternative is represented as a traversal along one branch of a user decision tree. The use of the framework is illustrated in two situations---searching on devices with small displays and for web search. Three well known RF algorithms, Rocchio, Robertson/Sparck-Jones (RSJ) and Bayesian, are compared for these applications. For small display devices, the algorithms are evaluated in conjunction with two strate gies for presenting search results: the top-D ranked documents and a document ranking that attempts to maximise information gain from the user's choices. Experimental results in dicate that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For the other two algorithms, the exploratory display that maximises information gain produces better results. A user study was conducted to evaluate the performance of the relevance feedback methods with real users and compare the results with the findings from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm, coupled with the sampled display, is the most effective. For web-search, two possible representations for web-pages are considered---the textual content of the page and the anchor text of hyperlinks into this page. Results indicate that there is a significant variation in the upper-bound performance of the three RF algorithms and that the Bayesian algorithm approaches the best possible. The relative performance of the three algorithms differed in the two sets of experiments. All other factors being constant, this difference in effectiveness was attributed to the fact that the datasets used in the two cases were different. Also, at a more general level, a relationship was observed between the performance of the original query and benefits of subsequent relevance feedback. The remainder of the thesis looks at properties that characterise sets of documents with the particular aim of identifying measures that are predictive of future performance of statis tical algorithms on these document sets. The central hypothesis is that a set of points (corresponding to documents) are difficult if they lack structure. Three properties are identified---the clustering tendency, sensitivity to perturbation and the local intrinsic dimensionality. The clustering tendency reflects the presence or absence of natural groupings within the data. Perturbation analysis looks at the sensitivity of the similarity metric to small changes in the input. The correlation present in sets of points is measured by the local intrinsic dimensionality therefore indicating the randomness present in them. These properties are shown to be useful for two tasks, namely, measuring the complexity of text datasets and for query performance prediction.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.719071  DOI: Not available
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