We studied the dynamic effects of information needs and social influence of tags in an exploratory search task. Although initially differences in information needs led to diversity in tag choices, this diversity disappeared as participants collaboratively tagged the same set of resources. Our findings are in general consistent with the notion that people conform to the collective interpretation...

Exploratory information seeking in a knowledge domain, especially in an interdisciplinary area, with the purpose of learning and getting a sense of the domain is a challenge that new researchers are constantly facing. To address this challenge, we suggest a new visualization-based approach for collaborative exploratory information seeking. First, we introduce ExplorationMap, which is a node-lin...

Effective access to information on the Web requires constant improvement in existing search, navigation and visualization approaches due to the size, complexity and dynamic nature of the web information space. We combine and extend personalization approaches, faceted browsers, graph-based visualization and tree-based history visualization in order to provide users with advanced information expl...

Whereas for the fact-retrieval searches, optimal paths to the documents containing the required information are crucial, learning and investigation activities lead to a more continuous and exploratory process with the knowledge acquired during this “journey” being essential as well. Therefore, information seeking systems should focus on providing cues that might make these explorations more eff...

In this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker’s intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-...

This paper addresses exploratory search in large collections of historical texts. By way of example, we apply our method to a collection of documents comprising dossiers of the former East-German Ministry for State Security, and classical texts. The bases of our approach are topic models, a class of algorithms that define and infer themes pervading the corpus as probability distributions over t...

In the summer of 2008 all University of North Carolina libraries switched from a traditional library catalog interface supporting text-based searching (TextOnly) to a text and facet-based interface (TextFacet) to improve users’ search experiences. This study seeks to understand the differences between these two interfaces and how they affect the search experience of the novice user. In this stu...

COMBINING INTENT MODELING and visual user interfaces can help users discover novel information and dramatically improve their information-exploration performance. Current-generation search engines serve billions of requests each day, returning responses to search queries in fractions of a second. They are great tools for checking facts and looking up information for which users can easily creat...

This paper presents a pattern search algorithm and its hybridization with a random descent search for solving bound constrained minimax problems. The herein proposed heuristic pattern search method combines the Hooke and Jeeves (HJ) pattern and exploratory moves with a randomly generated approximate descent direction. Two versions of the heuristic algorithm have been applied to several benchmar...

Most information retrieval (IR) systems consider relevance, usefulness, and quality of information objects (documents, queries) for evaluation, prediction, and recommendation, often ignoring the underlying search process of information seeking. This may leave out opportunities for making recommendations that analyze the search process and/or recommend alternative search process instead of objec...