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The success of recommender systems ultimately depends on the availability of comprehensive user profiles that accurately capture the interests of end-users. However, the automatic compilation of such profiles represents a complex learning task. In this paper, we focus on how accurate user profiles can be generated directly from analysing the behaviours of(More)
Internet search technology is largely based on exact-match retrieval. Such systems rely on the user to provide an adequate description of their requirements. However, most users submit poorly specified queries, leading to imprecise search results. Furthermore, there is no facility for personalising searches to reflect implicit likes and dislikes of users.(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)
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 rec-ommender system research has begun to look towards more conversational models of recommendation, where the user is actively(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)
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)
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)