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2015 | OriginalPaper | Chapter

Query Click and Text Similarity Graph for Query Suggestions

Authors : D. Sejal, K. G. Shailesh, V. Tejaswi, Dinesh Anvekar, K. R. Venugopal, S. S. Iyengar, L. M. Patnaik

Published in: Machine Learning and Data Mining in Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users’ need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion.

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Metadata
Title
Query Click and Text Similarity Graph for Query Suggestions
Authors
D. Sejal
K. G. Shailesh
V. Tejaswi
Dinesh Anvekar
K. R. Venugopal
S. S. Iyengar
L. M. Patnaik
Copyright Year
2015
Book
Machine Learning and Data Mining in Pattern Recognition

Print ISBN: 978-3-319-21023-0

Electronic ISBN: 978-3-319-21024-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-21024-7_22

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