In information retrieval, learning to rank constructs a machine-based ranking
model which given a query, sorts the search results by their degree of
relevance or importance to the query. Neural networks have been successfully
applied to this problem, and in this paper, we propose an attention-based deep
neural network which better incorporates different embeddings of the queries
and search results with an attention-based mechanism...
This model also applies a
decoder mechanism to learn the ranks of the search results in a listwise
fashion. The embeddings are trained with convolutional neural networks or the
word2vec model. We demonstrate the performance of this model with image
retrieval and text querying data sets.