learning to rank categories for web queries
In web search, understanding the user intent plays an important role in improving search experience of the end users. Such an intent can be represented by the categories which the user query belongs to. In this work, we propose an information retrieval based approach to query categorization with an emphasis on learning category rankings. To carry out categorization we first represent a category by web documents (from Open Directory Project) that describe the semantics of the category. Then, we learn the category rankings for the queries using 'learning to rank' techniques. To show that the results obtained are consistent and do not vary across datasets, we evaluate our approach on two datasets including the publicly available KDD Cup dataset. We report an overall improvement of 20\% on all evaluation metrics (precision, recall and F-measure) over two baselines: a text categorization baseline and an unsupervised IR baseline.
learning to rank query categorization query classification