Query recommendation based on query relevance graph

With the explosive and diverse growth of web contents, query recommendation is a critical aspect of the search engine. Different kind of recommendation like query, image, movie, music and book etc. are used every day. Different kinds of data 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. This paper presents a general method to recommend queries by combining two graphs: 1) query click graph which uses the knowledge of link between user input query and clicked 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. Experiment results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It is also