Personalized information ordering: a case study in online recruitment
Traditional search engine techniques are inadequate when it comes to helping the average user locate relevant information online. The key problem is their inability to recognize and respond to the implicit preferences of a user that are typically unstated in a search query. In this paper we describe CASPER, an online recruitment search engine, which attempts to address this issue by extending traditional search techniques with a personalization technique that is capable of taking account of user preferences as a means of classifying retrieved results as relevant or irrelevant. We evaluate a number of different classification strategies with respect to their accuracy and noise tolerance. Furthermore we argue that because CASPER transfers its personalization process to the client-side it offers significant efficiency and privacy advantages over more traditional server-side approaches.
personalised case retrieval web-based similarity search information reordering