Profile-Based Summarisation for Web Site Navigation

Abstract

Information systems that utilise contextual information have the potential of helping a user identify relevant information more quickly and more accurately than systems that work the same for all users and contexts. Contextual information comes in a variety of types, often derived from records of past interactions between a user and the information system. It can be individual or group based. We are focusing on the latter, harnessing the search behaviour of cohorts of users, turning it into a domain model that can then be used to assist other users of the same cohort. More specifically, we aim to explore how such a domain model is best utilised for profile-biased summarisation of documents in a navigation scenario in which such summaries can be displayed as hover text as a user moves the mouse over a link. The main motivation is to help a user find relevant documents more quickly. Given the fact that the Web in general has been studied extensively already, we focus our attention on Web sites and similar document collections. Such collections can be notoriously difficult to search or explore. The process of acquiring the domain model is not a research interest here; we simply adopt a biologically inspired method that resembles the idea of ant colony optimisation. This has been shown to work well in a variety of application areas. The model can be built in a continuous learning cycle that exploits search patterns as recorded in typical query log files. Our research explores different summarisation techniques, some of which use the domain model and some that do not. We perform task-based evaluations of these different techniques—thus of the impact of the domain model and profile-biased summarisation—in the context of Web site navigation.

Development of Relevance Feedback System using Regression Predictive Model and TF-IDF Algorithm

“…Also, the researchers did not have access to the database of related systems and so could not populate them with documents related to the simulated task. We, therefore decided to follow a similar evaluation approach used by Balakrishnan and Zhang [14] and Alhindi et al [27], where a self-designed system was used for the evaluation. In as much as the similar type of systems were not used for the evaluation, the self-designed systems were carefully designed without bias to achieve the objective of the research.…”

“…They thereby limit efficient recommendation of relevant documents to users of a particular domain. Contextualizing information retrieval potentially helps users to find relevant and accurate information within a minimal timeframe [27]. Context sensitive systems have been developed to improve web search.…”

Contextualised Browsing in a Digital Library's Living Lab

“…White et al [32] developed a task-based personalisation approach that utilises session history from other users with similar tasks referred to as groupisation. A contextualisation approach aiming to support browsing in a local web site is presented in [1]. Based on the implicit feedback of groups of users, a document summarisation for browsing is developed.…”

Social Search

“…In this way, highly personalized, query-focused snippets can be generated at a given length specification. In tests, these snippets prove to be superior to those produced by alternative summarization techniques, including those that involve sophisticated natural language processing techniques; see also Alhindi et al [9] for related ideas on generating group-adapted page snippets.…”