Algorithms for clustering data
Algorithms for clustering data
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Proceedings of the 18th ACM conference on Information and knowledge management
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Query similarity by projecting the query-flow graph
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A new mathematics retrieval system
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Addressing people's information needs directly in a web search result page
Proceedings of the 20th international conference on World wide web
NET – a system for extracting web data from flat and nested data records
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
Interpreting user inactivity on search results
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
A search engine for mathematical formulae
AISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Symbolic Computation
Retrieving documents with mathematical content
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Conventional search engines such as Bing and Google provide a user with a short answer to some queries as well as a ranked list of documents, in order to better meet her information needs. In this paper we study a class of such queries that we call math. Calculations (e.g. "12% of 24$ ", "square root of 120"), unit conversions (e.g. "convert 10 meter to feet"), and symbolic computations (e.g. "plot x^2+x+1") are examples of math queries. Among the queries that should be answered, math queries are special because of the infinite combinations of numbers and symbols, and rather few keywords that form them. Answering math queries must be done through real time computations rather than keyword searches or database look ups. The lack of a formal definition for the entire range of math queries makes it hard to automatically identify them all. We propose a novel approach for recognizing and classifying math queries using large scale search logs, and investigate its accuracy through empirical experiments and statistical analysis. It allows us to discover classes of math queries even if we do not know their structures in advance. It also helps to identify queries that are not math even though they might look like math queries. We also evaluate the usefulness of math answers based on the implicit feedback from users. Traditional approaches for evaluating the quality of search results mostly rely on the click information and interpret a click on a link as a sign of satisfaction. Answers to math queries do not contain links, therefore such metrics are not applicable to them. In this paper we describe two evaluation metrics that can be applied for math queries, and present the results on a large collection of math queries taken from Bing's search logs.