Rachael Rafter

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As it stands the Internet’s “one size fits all” approach to information retrieval presents the average user with a serious information overload problem. Adaptive hypermedia systems can provide a solution to this problem by learning about the implicit and explicit preferences of individual users and using this information to personalise information retrieval(More)
The aim of personalizing web information systems is to tailor content (media) to the user's personal preferences, goals and context, in turn increasing the reusability of that content. However, most developers are increasingly seeking to apply 'Web as a Platform' based approaches where web-based content is integrated with web services to provide the(More)
Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations is generated based on a user’s (past) stored preferences. However, content-based recommender system research has begun to look towards more conversational models of recommendation, where the user is actively engaged(More)
Collaborative filtering (CF) techniques have proved to be a powerful and popular component of modern recommender systems. Common approaches such as user-based and item-based methods generate predictions from the past ratings of users by combining two separate ratings components: a base estimate, generally based on the average rating of the target user or(More)
Online recruitment services suffer from shortcomings due to traditional search techniques. Most users fail to construct queries that provide an adequate and accurate description of their (job) requirements, leading to imprecise search results. We investigate one potential solution that combines implicit profiling methods and automated collaborative(More)
CASPER’s solution is a two-stage search engine (see the accompanying figure) that selects job cases not just according to their similarity to the target query, but also according to their relevance to the specific user in question, based on that user’s interaction history [1]. During stage one, job cases are ranked according to their similarity to the query(More)