Personalised search time prediction using Markov chains

Research output: Chapter in Book/Report/Conference proceedingConference contribution

For improving the effectiveness of Interactive Information Retrieval (IIR), a system should minimise the search time by guiding the user appropriately. As a prerequisite, in any search situation, the system must be able to estimate the time the user will need for finding the next relevant document. In this paper, we show how Markov models derived from search logs can be used for predicting search times, and describe a method for evaluating these predictions. For personalising the predictions based upon a few user events observed, we devise appropriate parameter estimation methods. Our experimental results show that by observing users for only 100 seconds, the personalised predictions are already significantly better than global predictions.

Original languageEnglish
Title of host publicationICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval
Pages237-240
Number of pages4
DOIs
StatePublished - 1 Oct 2017
Event7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017 - Amsterdam, Netherlands

Conference

Conference7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017
CountryNetherlands
CityAmsterdam
Period1/10/174/10/17

    Research areas

  • information retrieval, information systems, Markov chain, human computer interaction

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