One of the fundamental feature of web search engine is keyword suggestion. After submitting a keyword query, the user may not be satisfied with the results, so the keyword suggestion module of the search engine recommends a set of alternative keyword queries that are most likely to refine the user’s need. The suggested keywords are semantic relevance to keyword query. Spatial vicinity of user can be also consider to get suggestion in effective manner. In this paper, we develop location-aware keyword query suggestion framework considering the document distance and rating. The system uses keyword document graph for capturing semantic relevance between keyword queries and spatial distance of document and query issuers’ location. The keyword document graph is browsed in random walk with restart fashion, for calculating the highest score for better keyword query suggestion. The baseline algorithm and partition-based algorithm uses RWR to compute top-m suggestions and based upon users selected keyword query the documents are ranked using bayesian ranking method.

Keyword query suggestion, Spatial objects, Document proximity

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