The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Information retrieval on the web
Open accessAbstract
In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. We present data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. Although numerical figures vary, overall trends cited by the sources are consistent and point to exponential growth in the past and in the coming decade. Hence it is not surprising that about 85% of Internet users surveyed claim using search engines and search services to find specific information. The same surveys show, however, that users are not satisfied with the performance of the current generation of search engines; the slow retrieval speed, communication delays, and poor quality of retrieved results (e.g., noise and broken links) are commonly cited problems. We discuss the development of new techniques targeted to resolve some of the problems associated with Web-based information retrieval and speculate on future trends.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
An interactive method for accessing tables in HTML
Oogane, T., & Asakawa, C. (1998) · Proceedings of the third international ACM conference on Assistive technologies · doi:10.1145/274497.274521 · Cited by 38
Cited by
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., & Li, Q. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671470 · Cited by 827
Personalized e-learning system using Item Response Theory
Chen, C.-M., Lee, H.-M., & Chen, Y.-H. (2005) · Computers & Education · doi:10.1016/j.compedu.2004.01.006 · Cited by 482
Tang, H., Key, D., & Ellis, K. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-2243 · Cited by 20
slr-kit: A semi-supervised machine learning framework for systematic literature reviews
Facchinetti, T., Benetti, G., Giuffrida, D., & Nocera, A. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109266 · Cited by 12
Metadata
- Authors
- Kobayashi, M., & Takeda, K.
- Venue
- ACM Computing Surveys
- Published
- 2000-06
- Volume
- 32
- Issue
- 2
- Pages
- 144-173
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2046325278
- Cited by
- 562
- References
- 3
- FWCI
- 72.65
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Kobayashi, M., & Takeda, K. (2000). Information retrieval on the web. ACM Computing Surveys. https://doi.org/10.1145/358923.358934
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
