Results 47 resources
Yee, K.-P., Swearingen, K., Li, K., & Hearst, M. (2003). Faceted Metadata for Image Search and Browsing. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 401–408. https://doi.org/10.1145/642611.642681
There are currently two dominant interface types for searching and browsing large image collections: keyword-based search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.
Broder, A. (2002). A Taxonomy of Web Search. SIGIR Forum, 36(2), 3–10. https://doi.org/10.1145/792550.792552
Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.
Hearst, M., Elliott, A., English, J., Sinha, R., Swearingen, K., & Yee, K.-P. (2002). Finding the flow in web site search. Communications of the ACM, 45(9), 42–49. https://doi.org/10.1145/567498.567525
Designing a search system and interface may best be served (and executed) by scrutinizing usability studies.
Joachims, T. (2002). Optimizing search engines using clickthrough data. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 133–142. https://doi.org/10.1145/775047.775067
This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.
English, J., Hearst, M., Sinha, R., Swearingen, K., & Yee, K.-P. (2002). Hierarchical Faceted Metadata in Site Search Interfaces. CHI ’02 Extended Abstracts on Human Factors in Computing Systems, 628–639. https://doi.org/10.1145/506443.506517
One of the most pressing usability issues in the design of large web sites is that of the organization of search results. A previous study on a moderate-sized web site indicated that users understood and preferred dynamically organized faceted metadata over standard search. We are now examining how to scale this approach to very large collections, since it is difficult to present hierarchical faceted metadata in a manner appealing and understandable to general users. We have iteratively designed and tested interfaces that address these design challenges; the most recent version is receiving enthusiastic responses in ongoing usability studies.
Spink, A., Wolfram, D., Jansen, M. B. J., & Saracevic, T. (2001). Searching the web: The public and their queries. Journal of the American Society for Information Science and Technology, 52(3), 226–234. https://doi.org/10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R
In studying actual Web searching by the public at large, we analyzed over one million Web queries by users of the Excite search engine. We found that most people use few search terms, few modified queries, view few Web pages, and rarely use advanced search features. A small number of search terms are used with high frequency, and a great many terms are unique; the language of Web queries is distinctive. Queries about recreation and entertainment rank highest. Findings are compared to data from two other large studies of Web queries. This study provides an insight into the public practices and choices in Web searching.
Jansen, B. J., Spink, A., & Saracevic, T. (2000). Real life, real users, and real needs: a study and analysis of user queries on the web. Information Processing & Management, 36(2), 207–227. https://doi.org/10.1016/S0306-4573(99)00056-4
We analyzed transaction logs containing 51,473 queries posed by 18,113 users of Excite, a major Internet search service. We provide data on: (i) sessions — changes in queries during a session, number of pages viewed, and use of relevance feedback; (ii) queries — the number of search terms, and the use of logic and modifiers; and (iii) terms — their rank/frequency distribution and the most highly used search terms. We then shift the focus of analysis from the query to the user to gain insight to the characteristics of the Web user. With these characteristics as a basis, we then conducted a failure analysis, identifying trends among user mistakes. We conclude with a summary of findings and a discussion of the implications of these findings.