Recommendation in Web Search Using Similar Query Mining with Big Data

The queries submitted to web search engines are often short. Consequently, the search intents are obscure and hard to understand in the systems. In order to provide a more personalized service for each user incorporating his/her interests, query suggestion models are employed by formulating similar queries to retrieve more rich and personalized web pages. In this paper, a novel query suggestion model is proposed. It mines similar queries in similar query sessions from search engine logs and re-ranks them. The ranking order of the recommendations is evaluated with the help of the ranking measure: normalized discounted cumulative gain (NDCG). Results show that it is effective.

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