Browsing by Author "Shailesh, K.G."

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QRGQR: Query relevance graph for query recommendation
(2015) Sejal, D.; Shailesh, K.G.; Tejaswi, V.; Anvekar, D.; Venugopal, K.R.; Iyengar, S.S.; Patnaik, L.M.
Query recommendation is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of recommendation like query, image, movies, music and book etc. Are used every day. Various types of data sources are used for the recommendations. If we model the data into various kinds of graphs then we can build a general method for any recommendation. In this paper, we have proposed a general method for query recommendation 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 recommendation. � 2015 IEEE.
QRGQR: Query relevance graph for query recommendation
(Institute of Electrical and Electronics Engineers Inc., 2015) Sejal, D.; Shailesh, K.G.; Tejaswi, V.; Anvekar, D.; Venugopal, K.R.; Iyengar, S.S.; Patnaik, L.M.
Query recommendation is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of recommendation like query, image, movies, music and book etc. Are used every day. Various types of data sources are used for the recommendations. If we model the data into various kinds of graphs then we can build a general method for any recommendation. In this paper, we have proposed a general method for query recommendation 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 recommendation. Â© 2015 IEEE.
Query click and text similarity graph for query suggestions
(2015) Sejal, D.; Shailesh, K.G.; Tejaswi, V.; Anvekar, D.; Venugopa, K.R.; Iyengar, S.S.; Patnaik, L.M.
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. � Springer International Publishing Switzerland 2015.
Query click and text similarity graph for query suggestions
(Springer Verlag service@springer.de, 2015) Sejal, D.; Shailesh, K.G.; Tejaswi, V.; Anvekar, D.; Venugopa, K.R.; Iyengar, S.S.; Patnaik, L.M.
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 usersâ€™ 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. Â© Springer International Publishing Switzerland 2015.