AN ATTENTION-BASED DEEP NET FOR LEARNING TO RANK

Diego Klabjan, Baiyang Wang

Keywords: learning to rank, deep learning

TL;DR: learning to rank with several embeddings and attentions

Abstract: 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.