  This paper presents a novel query clustering approach to capture the broad
interest areas of users querying search engines. We make use of recent advances
in NLP - word2vec and extend it to get query2vec, vector representations of
queries, based on query contexts, obtained from the top search results for the
query and use a highly scalable Divide & Merge clustering algorithm on top of
the query vectors, to get the clusters. We have tried this approach on a
variety of segments, including Retail, Travel, Health, Phones and found the
clusters to be effective in discovering user's interest areas which have high
monetization potential.
