We examine the effect of incorporating gaze-based attention feedback from the user on personalizing the search process. Employing eye tracking data, we keep track of document parts the user read in some way. We use this information on the subdocument level as implicit feedback for query expansion and reranking.We evaluated three different variants incorporating gaze data on the subdocument level and compared them against a baseline based on context on the document level. Our results show that considering reading behavior as feedback yields powerful improvements of the search result accuracy of ca. 32% in the general case. However, the extent of the improvements varies depending on the internal structure of the viewed documents and the type of the current information need.

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Abstract. In this paper, we outline the relation between Knowledge Management (KM) as an application area on the one hand, and software agents as a basic technology for supporting KM on the other. We start by presenting characteristics of KM which account for some drawbacks of today's -typically centralized -technological approaches for KM. We argue that the basic features of agents (social ability, autonomy, re-and proactiveness) can alleviate several of these drawbacks. A classification schema for the description of agent-based KM systems is established, and a couple of example systems are depicted in terms of this schema. The paper concludes with questions which we think research in Agentmediated Knowledge Management (AMKM) should deal with.

Reading is one of the most frequent activities of knowledge workers. Eye tracking can provide information on what document parts users read, and how they were read. This article aims at generating implicit relevance feedback from eye movements that can be used for information retrieval personalization and further applications. We report the findings from two studies which examine the relation between several eye movement measures and user-perceived relevance of read text passages. The results show that the measures are generally noisy, but after personalizing them we find clear relations between the measures and relevance. In addition, the second study demonstrates the effect of using reading behavior as implicit relevance feedback for personalizing search. The results indicate that gaze-based feedback is very useful and can greatly improve the quality of Web search. The article concludes with an outlook introducing attentive documents keeping track of how users consume them. Based on eye movement feedback, we describe a number of possible applications to make working with documents more effective.