Journal ArticleDOI
A taxonomy of web search
Andrei Z. Broder
- Vol. 36, Iss: 2, pp 3-10
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TLDR
This taxonomy of web searches is explored and how global search engines evolved to deal with web-specific needs is discussed.Abstract:
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.read more
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Proceedings ArticleDOI
Why we twitter: understanding microblogging usage and communities
TL;DR: It is found that people use microblogging to talk about their daily activities and to seek or share information and the user intentions associated at a community level are analyzed to show how users with similar intentions connect with each other.
Book
Learning to Rank for Information Retrieval
TL;DR: Three major approaches to learning to rank are introduced, i.e., the pointwise, pairwise, and listwise approaches, the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures are analyzed, and the performance of these approaches on the LETOR benchmark datasets is evaluated.
Journal IssueDOI
Twitter power: Tweets as electronic word of mouth
TL;DR: It is found that microblogting is an online tool for customer word of mouth communications and the implications for corporations using microblogging as part of their overall marketing strategy are discussed.
Proceedings ArticleDOI
Accurately interpreting clickthrough data as implicit feedback
TL;DR: It is concluded that clicks are informative but biased, and while this makes the interpretation of clicks as absolute relevance judgments difficult, it is shown that relative preferences derived from clicks are reasonably accurate on average.
References
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Journal ArticleDOI
The anatomy of a large-scale hypertextual Web search engine
Sergey Brin,Lawrence Page +1 more
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Journal Article
The Anatomy of a Large-Scale Hypertextual Web Search Engine.
Sergey Brin,Lawrence Page +1 more
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
Book
Foundations of Statistical Natural Language Processing
TL;DR: This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear and provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods
TL;DR: SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets.
Posted Content
Estimating Continuous Distributions in Bayesian Classifiers
George H. John,Pat Langley +1 more
TL;DR: This paper abandon the normality assumption and instead use statistical methods for nonparametric density estimation for kernel estimation, which suggests that kernel estimation is a useful tool for learning Bayesian models.