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Predicting user behaviour on a website is a difficult task, which requiresthe integration of multiple sources of information, such as geo-location, userprofile or web surfing history. In this paper we tackle the problem ofpredicting the user intent, based on the queries that were used to access acertain webpage. We make no additional assumptions, such as domain detection,device used or location, and only use the word information embedded in thegiven query. In order to build competitive classifiers, we label a smallfraction of the EDI query intent prediction dataset\cite{edi-challenge-dataset}, which is used as ground truth. Then, usingvarious rule-based approaches, we automatically label the rest of the dataset,train the classifiers and evaluate the quality of the automatic labeling on theground truth dataset. We used both recurrent and convolutional networks as themodels, while representing the words in the query with multiple embeddingmethods.

Predicting user intent from search queries using both CNNs and RNNs