Search

In interactive information-seeking, a user often performs many interrelated queries and interactions covering multiple aspects of a broad topic of interest. Especially in difficult information-seeking tasks the user may need to find what is in common among such multiple aspects. Therefore, the user may need to compare and combine results across queries. While methods to combine queries or rankings have been proposed, little attention has been paid to interactive support for combining multiple queries in exploratory search. We introduce an interactive information retrieval system for exploratory search with multiple simultaneous search queries that can be combined. The user is able to direct search in the multiple queries, and combine queries by two operations: intersection and difference, which reveal what is relevant to the user intent of two queries, and what is relevant to one but not the other. Search is directed by relevance feedback on visualized user intent models of each query. Operations on queries act directly on the intent models inferring a combined user intent model. Each combination yields a new result (ranking) and acts as a new search that can be interactively directed and further combined. User experiments on difficult information-seeking tasks show that our novel system with query operations yields more relevant top-ranked documents in a shorter time than a baseline multiple-query system. acceptedVersion

In difficult information seeking tasks, the majority of topranked documents for an initial query may be non-relevant, and negative relevance feedback may then help find relevant documents. Traditional negative relevance feedback has been studied on document results; we introduce a system and interface for negative feedback in a novel exploratory search setting, where continuous-valued feedback is directly given to keyword features of an inferred probabilistic user intent model. The introduced system allows both positive and negative feedback directly on an interactive visual interface, by letting the user manipulate keywords on an optimized visualization of modeled user intent. Feedback on the interactive intent model lets the user direct the search: Relevance of keywords is estimated from feedback by Bayesian inference, influence of feedback is increased by a novel propagation step, documents are retrieved by likelihoods of relevant versus non-relevant intents, and the most relevant keywords (having the highest upper confidence bounds of relevance) and the most non-relevant ones (having the smallest lower confidence bounds of relevance) are shown as options for further feedback. We carry out task-based information seeking experiments with real users on difficult real tasks; we compare the system to the nearest state of the art baseline allowing positive feedback only, and show negative feedback significantly improves the quality of retrieved information and user satisfaction for difficult tasks.