Models in Interactive Information Retrieval (IIR) are grounded very much on the users task in order to give system support based on different task types and topics. However, the automatic recognition of user interests from log data in search systems is not trivial. Search queries entered by users a surely one such source. However, queries may be short, or users are only browsing. In this paper, we propose a method of term-mouse-fixations which takes the fixations on terms users are hovering over with the mouse into consideration to estimate topical user interests. We analyzed 22,259 search sessions of a domain-specific digital library over a period of about four months. We compared these mouse fixations to user-entered search terms and to titles and keywords from documents the user showed an interest in. These terms were found in 87.12% of all analyzed sessions; in this subset of sessions, per session on average 11.46 term-mouse-fixations from queries and viewed documents were found. These terms were fixated significantly longer with about 7 seconds than other terms with about 4.4 seconds. This means, term-mouse-fixations provide indicators for topical user interests and it is possible to extract them based on fixation time.
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