Reformulation of tail queries for product searches

Abstract

Technologies are provided for reformulation of a tail query to a head query with the same purchase intent by mapping the tail query to the head query. In some of the technologies, a reasonable embedding can be learned on historical head queries. The embedding can then be refined by leveraging rewards generated from a persistently noisy oracle that compensates for the lack of historical behavioral signal for tail queries. Further, a contextual sampling technique that uses text-based rewards or oracle-based rewards, or both, can be implemented in order to avoid biases introduced by persistent noise in the oracle. Numerical experiments on large scale e-commerce datasets demonstrate that the provided technologies can outperform several conventional approaches to query reformulation.