Utilizing a geometry of context for enhanced implicit feedback

Implicit feedback algorithms utilize interaction between searchers and search systems to learn more about users’ needs and interests than expressed in query statements alone. This additional information can be used to formulate improved queries or directly improve retrieval performance. In this paper we present a geometric framework that utilizes multiple sources of evidence present in this interaction context (e.g., display time, document retention) to develop enhanced implicit feedback models personalized for each user and tailored for each search task. We use rich interaction logs (and associated metadata such as relevance judgments), gathered during a longitudinal user study, as relevance stimuli to compare an implicit feedback algorithm developed using the framework with alternative algorithms. Our findings demonstrate both the effectiveness of our proposed algorithm and the potential value of incorporating multiple sources of interaction evidence when developing implicit fee...

• Type: Conference
• Year: 2007
• Where: CIKM
• Authors: Massimo Melucci, Ryen W. White
• Keywords: CIKM 2007, Implicit Feedback, Implicit Feedback Algorithms, Implicit Feedback Models