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Query Click and Text Similarity Graph for Query Suggestions

Sejal, D and Shailesh, KG and Tejaswi, V and Anvekar, Dinesh and Venugopal, KR and Iyengar, SS and Patnaik, LM (2015) Query Click and Text Similarity Graph for Query Suggestions. In: 11th International Conference on Machine Learning and Data Mining (MLDM), JUL 20-21, 2015, Hamburg, GERMANY, pp. 328-341.

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Official URL: http://dx.doi.org/10.1007/978-3-319-21024-7_22


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.

Item Type: Conference Proceedings
Series.: Lecture Notes in Artificial Intelligence
Additional Information: Copy right for this article belongs to the SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
Keywords: Image suggestion; Query suggestion; Query relevance; Recommendation
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 30 Dec 2015 06:06
Last Modified: 30 Dec 2015 06:06
URI: http://eprints.iisc.ac.in/id/eprint/52962

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