Findex: improving search result use through automatic filtering categories

Abstract

Long result lists from web search engines can be tedious to use. We designed a text categorization algorithm and a filtering user interface to address the problem. The Findex system provides an overview of the results by presenting a list of the most frequent words and phrases as result categories next to the actual results. Selecting a category (word or phrase) filters the result list to show only the results containing it. An experiment with 20 participants was conducted to compare the category design to the de facto standard solution (Google-type ranked list interface). Results show that the users were 25% faster and 21% more accurate with our system. In particular, participants' speed of finding relevant results was 40% higher with the proposed system. Subjective ratings revealed significantly more positive attitudes towards the new system. Results indicate that the proposed design is feasible and beneficial. q

Mobile Findex: Facilitating Information Access in Mobile Web Search with Automatic Result Clustering

“…Flamenco, a hierarchical faceted metadata interface by Yee et al [17] and automatic classification approaches, such as SWISH [16] by Chen and Dumais, provide hierarchical category structures with descriptive category labels to support the exploration of search results. In contrast, many proposed clustering approaches [18][19][20] produce a flat list of cluster labels. However, also hierarchical clustering techniques have been proposed, for example, Ferragina and Gulli [21] introduced SnakeT, which uses gapped sentences from text instead of single terms or phrases as labels for the result clusters.…”

“…We use the Findex clustering algorithm [19] and its software implementation, the clustering engine, to execute search queries and generate result categories. The clustering engine is implemented as a Java component that can be integrated into both standalone applications and Web services.…”

“…The evaluation was organized as a laboratory experiment, in which the different factors affecting the usage situation could be controlled. The experimental setting is based on a previous experiment by Käki and Aula [19], in which a category interface was compared to ranked result lists in the context of desktop Web search.…”

“…Although using predefined queries and cached results lowers the fidelity of the setting, it enabled us to draw comparisons between the interfaces. This approach is also used in previous studies comparing search user interface designs (e.g., [16,19]). No special query operators, such as Boolean logic or parentheses, were used as a part of the queries, given their very low popularity in general use (only about 3% of queries contain special operators [4]).…”

Evaluating Usability of a Long Query Meta Search Engine

“…Because search engines regularly return millions of hits, a search engine that filters search results for user relevance evaluation [13,24] provides welcome help and improves user satisfaction.…”