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Personalised Information Retrieval: survey and classification

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Abstract

Information Retrieval (IR) systems assist users in finding information from the myriad of information resources available on the Web. A traditional characteristic of IR systems is that if different users submit the same query, the system would yield the same list of results, regardless of the user. Personalised Information Retrieval (PIR) systems take a step further to better satisfy the user’s specific information needs by providing search results that are not only of relevance to the query but are also of particular relevance to the user who submitted the query. PIR has thereby attracted increasing research and commercial attention as information portals aim at achieving user loyalty by improving their performance in terms of effectiveness and user satisfaction. In order to provide a personalised service, a PIR system maintains information about the users and the history of their interactions with the system. This information is then used to adapt the users’ queries or the results so that information that is more relevant to the users is retrieved and presented. This survey paper features a critical review of PIR systems, with a focus on personalised search. The survey provides an insight into the stages involved in building and evaluating PIR systems, namely: information gathering, information representation, personalisation execution, and system evaluation. Moreover, the survey provides an analysis of PIR systems with respect to the scope of personalisation addressed. The survey proposes a classification of PIR systems into three scopes: individualised systems, community-based systems, and aggregate-level systems. Based on the conducted survey, the paper concludes by highlighting challenges and future research directions in the field of PIR.

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  1. Centre for Next Generation Localisation, Knowledge & Data Engineering Group, School of Computer Science & Statistics, Trinity College Dublin, College Green, Dublin 2, Ireland

    M. Rami Ghorab, Dong Zhou, Alexander O’Connor & Vincent Wade

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  1. M. Rami Ghorab
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  2. Dong Zhou
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  3. Alexander O’Connor
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  4. Vincent Wade
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Correspondence to M. Rami Ghorab.

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Ghorab, M.R., Zhou, D., O’Connor, A. et al. Personalised Information Retrieval: survey and classification. User Model User-Adap Inter 23, 381–443 (2013). https://doi.org/10.1007/s11257-012-9124-1

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