Purpose-This study aims to examine the differences between web image and textual queries. Design/methodology/approach-A large number of web queries from image and textual search engines were analysed and compared based on their factual characteristics, query types, and search interests. Findings-Useful results include the findings that web users tend to input short queries when searching for visual or textual information; that image requests have more zero hits and higher specificity, and contain more refined queries; that web image requests are more focused than textual requests on some popular search interests, and that the variety of textual queries is greater than that of image requests. Originality/value-This study provides results that may enhance one's understanding of web-searching behaviour and the inherent implications for the improvement of current web image retrieval systems.
This study examines the differences between Web image and textual queries, and attempts to develop an analytic model to investigate their implications for Web image retrieval systems. A large number of Web queries from image and textual search engines were analyzed and compared based on their factual characteristics, query types, and search interests. A feasible analytic model employing the concepts of uniqueness and refinement was adapted to categorize query types and analyze the characteristics of failed queries. Useful results include the findings that image requests may have higher specificity and contain more refined queries (especially among failed queries), and that the queries were refined more by interpretive attributes than by reactive and perceptual attributes. Current text retrieval technology is not capable of fulfilling such complex image requests. It is suggested that there is a need to increase the number of appropriate annotations for Web images and to utilize more advanced retrieval techniques for more effective Web image searching. Few previous large-scale studies have investigated visual information retrieval using image search engines. Thus, this study provides results that might enhance our understanding of Web image searching behavior and suggests implications for the improvement of current Web image search engines.
This paper explores the possibility of adding user-oriented class associations to hierarchical library classification schemes. Some highly associated classes not grouped in the same subject hierarchies, yet relevant to users' knowledge, are automatically obtained by analyzing a two-year log of book circulation records from a university library in Taiwan. The library uses the Chinese Decimal Classification scheme, which has similar structure and notation to the Dewey Decimal Classification. Methods, from both collaborative filtering and information retrieval research, were employed and their performance compared based on similarity estimation of classes. The results show that classification schemes can, therefore, be made more adaptable to changes of users and the uses of different library collections by analyzing the circulation patterns of similar users. Limitations of the methods and implications for applications are also discussed.
This study attempts to characterize users' sociality in tagging by clustering factors that users consider when selecting tags for their bookmarks. Twenty-three frequent users of social bookmarking services were invited, and Q method and factor analysis have been applied in the study. Each cluster of users has varied focuses on personal, content, situational, and social factors when tagging. The study explores the four distinct types of sociality in tagging by the orientation of tag selection and the complexity of affecting factors. The results show different tendencies of sociality in tagging emerge, i.e., tag for me, us, all, and mixed. As there is the shift from functionality towards sociality for the social tagging applications, the initial findings may benefit the future related research.
This paper presents an approach to investigating the possibility for constructing an automatic and scalable thesaurus based on Web users' vocabularies with search interests. The proposed approach mainly includes two techniques, namely, relevant term extraction and concept clustering. The former combines query-session-based and text-based methods to extract relevant terms for a given search term; and the latter organizes these relevant terms into concept classes based on the search results from search engines. Some initial experiments have been conducted to test feasibility of the proposed approach to organizing Web users' vocabularies.The obtained results show that relevant terms could be extracted efficiently and concept classes be more well organized. The approach has a great potential to benefit the automatic construction of a large scale thesaurus for future Web IR applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.