In this paper, we present QSEGMENT, a real-life query segmentation system for eCommerce queries. QSEGMENT uses frequency data from the query log which we call buyers′ data and also frequency data from product titles what we call sellers′ data.
We exploit the taxonomical structure of the marketplace to build domain speciﬁc frequency models. Using such an approach, QSEGMENT performs better than previously described baselines for query segmentation.
Also, we perform a large scale evaluation by using an unsupervised IR metric which we refer to as user-intent-score. We discuss the overall architecture of QSEGMENT as well as various use cases and interesting observations around segmenting eCommerce queries.