Academic journal article Information Technology and Libraries

Autocomplete as a Research Tool: A Study on Providing Search Suggestions

Academic journal article Information Technology and Libraries

Autocomplete as a Research Tool: A Study on Providing Search Suggestions

Article excerpt

ABSTRACT

As the library website and its online searching tools become the primary "branch" many users visit for their research, methods for providing automated, context-sensitive research assistance need to be developed to guide unmediated searching toward the most relevant results. This study examines one such method, the use of autocompletion in search interfaces, by conducting usability tests on its use in typical academic research scenarios. The study reports notable findings on user preference for autocomplete features and suggests best practices for their implementation.

INTRODUCTION

Autocompletion, a searching feature that offers suggestions for search terms as a user types text in a search box (see figure 1), has become ubiquitous on both larger search engines as well as smaller, individual sites. Debuting as the "Google Suggest" feature in 2004 (1), autocomplete has made inroads into the library realm through inclusion in vendor search interfaces, including the most recent ProQuest interface and in EBSCO products. As this feature expands its presence in the library realm, it is important to understand how patrons include it in their workflow and the implications for library site design as well as for reference, instruction, and other library services.

An analysis of search logs from our library federated searching tool reveals both common errors in how search queries are entered, as well as patterns in the use of library search tools. For example, spelling suggestions are offered for more than 29 percent of all searches, and more than half (51 percent) of all searches appear to be for known items. (2) Additionally, punctuation such as commas and a variety of correct and incorrect uses of Boolean operators are prevalent. These patterns suggest that providing some form of guidance in keyword selection at the point of search-term entry could improve the accuracy of composing searches and subsequently the relevance of search results.

This study investigates student use of an autocompletion implementation on the initial search entry box for a library's primary federated searching feature. Through usability studies, the authors analyzed how and when students use autocompletion as part of typical library research, asked the students to assess the value and role of autocompletion in the research process, and noted any drawbacks of implementing the feature. Additionally, the study sought to analyze how implementing autocompletion on the front end of a search affected providing search suggestions on the back end (search result pages).

[FIGURE 1 OMITTED]

LITERATURE REVIEW

Autocomplete as a plug-in has become ubiquitous on site searches large and small. Research on autocomplete includes a variety of technical terms that refer to systems using this architecture. Examples include Real Time Query Expansion (RTQE), interactive query expansion, Search-as-you-Type (SayT), query completion, type-ahead search, auto-suggest, and suggestive searching/search suggestions. The principal research concerns for autocomplete include issues related to both back-end architecture and assessments of user satisfaction and systems for specific implementations.

Nandi and Jagadish present a detailed system architecture model for their implementation of autocomplete, which highlights many of the concerns and desirable features of constructing an index that the autocomplete will query against. (3) They note in particular that the quality of suggestions presented to the user must be high to compensate for the user interface distraction of having suggestions appear as a user types. This concern is echoed by Hanmin et al. in their analysis of how the results offered by their autocomplete implementation met user expectations. (4) Their findings emphasize configuring systems to display only keywords that bring about successful searches, noting "precision [of suggested terms] is closely related with satisfaction. …

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