| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Fu, T., Song, Q. & Chiu, D. | The academic social network | 2014 | Scientometrics Vol. 101(1), pp. 203-239 |
article | DOI URL |
| Abstract: By means of their academic publications, authors form a social network. Instead of sharing casual thoughts and photos (as in Facebook), authors select co-authors and reference papers written by other authors. Thanks to various efforts (such as Microsoft Academic Search and DBLP), the data necessary for analyzing the academic social network is becoming more available on the Internet. What type of information and queries would be useful for users to discover, beyond the search queries already available from services such as Google Scholar? In this paper, we explore this question by defining a variety of ranking metrics on different entities—authors, publication venues, and institutions. We go beyond traditional metrics such as paper counts, citations, and h-index. Specifically, we define metrics such as | |||||
BibTeX:
@article{fu2014academic,
author = {Fu, TomZ.J. and Song, Qianqian and Chiu, DahMing},
title = {The academic social network},
journal = {Scientometrics},
publisher = {Springer Netherlands},
year = {2014},
volume = {101},
number = {1},
pages = {203-239},
url = {http://dx.doi.org/10.1007/s11192-014-1356-x},
doi = {http://dx.doi.org/10.1007/s11192-014-1356-x}
}
|
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| Krause, B., Hotho, A. & Stumme, G. | A comparison of social bookmarking with traditional search | 2008 | Proceedings of the IR research, 30th European conference on Advances in information retrieval, pp. 101-113 | inproceedings | URL |
| Abstract: Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system's data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users.</p> <p>In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.</p> <p>Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system. | |||||
BibTeX:
@inproceedings{krause2008comparison,
author = {Krause, Beate and Hotho, Andreas and Stumme, Gerd},
title = {A comparison of social bookmarking with traditional search},
booktitle = {Proceedings of the IR research, 30th European conference on Advances in information retrieval},
publisher = {Springer-Verlag},
year = {2008},
pages = {101--113},
url = {http://dl.acm.org/citation.cfm?id=1793274.1793290}
}
|
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| Millen, D.R., Yang, M., Whittaker, S. & Feinberg, J. | Social bookmarking and exploratory search | 2007 | ECSCW 2007, pp. 21-40 | incollection | DOI URL |
| Abstract: In this paper, we explore various search tasks that are supported by a social bookmarking service. These bookmarking services hold great potential to powerfully combine personal tagging of information sources with interactive browsing, resulting in better social navigation. While there has been considerable interest in social tagging systems in recent years, little is known about their actual usage. In this paper, we present the results of a field study of a social bookmarking service that has been deployed in a large enterprise. We present new qualitative and quantitative data on how a corporate social tagging system was used, through both event logs (click level analysis) and interviews. We observed three types of search activities: community browsing, personal search, and explicit search. Community browsing was the most frequently used, and confirms the value of the social aspects of the system. We conclude that social bookmarking services support various kinds of exploratory search, and provide better personal bookmark management and enhance social navigation. | |||||
BibTeX:
@incollection{millen2007social,
author = {Millen, David R. and Yang, Meng and Whittaker, Steven and Feinberg, Jonathan},
title = {Social bookmarking and exploratory search},
booktitle = {ECSCW 2007},
publisher = {Springer London},
year = {2007},
pages = {21-40},
url = {http://dx.doi.org/10.1007/978-1-84800-031-5_2},
doi = {http://dx.doi.org/10.1007/978-1-84800-031-5_2}
}
|
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| Jansen, B.J. | Search log analysis: What it is, what's been done, how to do it | 2006 | Library & Information Science Research Vol. 28(3), pp. 407 - 432 |
article | DOI URL |
| Abstract: The use of data stored in transaction logs of Web search engines, Intranets, and Web sites can provide valuable insight into understanding the information-searching process of online searchers. This understanding can enlighten information system design, interface development, and devising the information architecture for content collections. This article presents a review and foundation for conducting Web search transaction log analysis. A methodology is outlined consisting of three stages, which are collection, preparation, and analysis. The three stages of the methodology are presented in detail with discussions of goals, metrics, and processes at each stage. Critical terms in transaction log analysis for Web searching are defined. The strengths and limitations of transaction log analysis as a research method are presented. An application to log client-side interactions that supplements transaction logs is reported on, and the application is made available for use by the research community. Suggestions are provided on ways to leverage the strengths of, while addressing the limitations of, transaction log analysis for Web-searching research. Finally, a complete flat text transaction log from a commercial search engine is available as supplementary material with this manuscript. | |||||
BibTeX:
@article{jansen2006search,
author = {Jansen, Bernard J.},
title = {Search log analysis: What it is, what's been done, how to do it},
journal = {Library & Information Science Research},
year = {2006},
volume = {28},
number = {3},
pages = {407 - 432},
url = {http://www.sciencedirect.com/science/article/pii/S0740818806000673},
doi = {http://dx.doi.org/10.1016/j.lisr.2006.06.005}
}
|
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| Park, S., Lee, J.H. & Bae, H.J. | End user searching: A Web log analysis of NAVER, a Korean Web search engine | 2005 | Library & Information Science Research Vol. 27(2), pp. 203 - 221 |
article | DOI URL |
| Abstract: Transaction logs of NAVER, a major Korean Web search engine, were analyzed to track the information-seeking behavior of Korean Web users. These transaction logs include more than 40 million queries collected over 1 week. This study examines current transaction log analysis methodologies and proposes a method for log cleaning, session definition, and query classification. A term definition method which is necessary for Korean transaction log analysis is also discussed. The results of this study show that users behave in a simple way: they type in short queries with a few query terms, seldom use advanced features, and view few results' pages. Users also behave in a passive way: they seldom change search environments set by the system. It is of interest that users tend to change their queries totally rather than adding or deleting terms to modify the previous queries. The results of this study might contribute to the development of more efficient and effective Web search engines and services. | |||||
BibTeX:
@article{park2005searching,
author = {Park, Soyeon and Lee, Joon Ho and Bae, Hee Jin},
title = {End user searching: A Web log analysis of NAVER, a Korean Web search engine},
journal = {Library & Information Science Research},
year = {2005},
volume = {27},
number = {2},
pages = {203 - 221},
url = {http://www.sciencedirect.com/science/article/pii/S0740818805000083},
doi = {http://dx.doi.org/10.1016/j.lisr.2005.01.013}
}
|
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| Rose, D.E. & Levinson, D. | Understanding user goals in web search | 2004 | Proceedings of the 13th international conference on World Wide Web, pp. 13-19 | inproceedings | DOI URL |
| Abstract: Previous work on understanding user web search behavior has focused on how people search and what they are searching for, but not why they are searching. In this paper, we describe a framework for understanding the underlying goals of user searches, and our experience in using the framework to manually classify queries from a web search engine. Our analysis suggests that so-called navigational" searches are less prevalent than generally believed while a previously unexplored "resource-seeking" goal may account for a large fraction of web searches. We also illustrate how this knowledge of user search goals might be used to improve future web search engines. | |||||
BibTeX:
@inproceedings{rose2004understanding,
author = {Rose, Daniel E. and Levinson, Danny},
title = {Understanding user goals in web search},
booktitle = {Proceedings of the 13th international conference on World Wide Web},
publisher = {ACM},
year = {2004},
pages = {13--19},
url = {http://doi.acm.org/10.1145/988672.988675},
doi = {http://dx.doi.org/10.1145/988672.988675}
}
|
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| Silverstein, C., Marais, H., Henzinger, M. & Moricz, M. | Analysis of a very large web search engine query log | 1999 | SIGIR Forum Vol. 33(1), pp. 6-12 |
article | DOI URL |
| Abstract: In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such. | |||||
BibTeX:
@article{silverstein1999analysis,
author = {Silverstein, Craig and Marais, Hannes and Henzinger, Monika and Moricz, Michael},
title = {Analysis of a very large web search engine query log},
journal = {SIGIR Forum},
publisher = {ACM},
year = {1999},
volume = {33},
number = {1},
pages = {6--12},
url = {http://doi.acm.org/10.1145/331403.331405},
doi = {http://dx.doi.org/10.1145/331403.331405}
}
|
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Created by JabRef on 24/04/2024.