Paper Info
Title
Query Click and Text Similarity Graph for Query Suggestions.
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.
Year
DOI
Venue
2015
10.1007/978-3-319-21024-7_22
MLDM
Keywords
Field
DocType
Image suggestion,Query suggestion,Query relevance,Recommendation
Query optimization,Web search query,RDF query language,Query language,Query expansion,Information retrieval,Computer science,Sargable,Web query classification,Spatial query
Conference
Volume
ISSN
Citations 
9166
0302-9743
0
PageRank 
References 
Authors
0.34
21
7
Name
Order
Citations
PageRank
D. Sejal141.83
K. G. Shailesh200.34
V. Tejaswi322.76
Dinesh Anvekar401.35
K. R. Venugopal526748.80
S.S. Iyengar62923381.93
Lalit M. Patnaik724348.76