Generalized Learning of Neural Network Based Semantic Similarity Models and Its Application in Movie Search

Abstract

Modeling text semantic similarity via neural network approaches has significantly improved performance on a set of information retrieval tasks in recent studies. However these neural network based latent semantic models are mostly trained by using simple user behavior logging data such as clicked (query, document)-pairs, and all the clicked pairs are assumed to be uniformly positive examples. Therefore, the existing method for learning the model parameters does not differentiate data samples that might reflect different relevance information. In this paper, we relax this assumption and propose a new learning method through a generalized loss function to capture the subtle relevance differences of training samples when a more granular label structure is available. We have applied it to the Xbox One's movie search task where session-based user behavior information is available and the granular relevance differences of training samples are derived from the session logs. Compared with the existing method, our new generalized loss function has demonstrated superior test performance measured by several user-engagement metrics. It also yields significant performance lift when the score computed from our model is used as a semantic similarity feature in the gradient boosted decision tree model which is widely used in modern search engines.