Incorporating Semantic Knowledge into Latent Matching Model in Search

TL;DR

This paper enhances latent matching models in search by integrating semantic knowledge, improving accuracy especially for tail queries with limited click data, through novel regularization techniques and optimization methods.

Contribution

It introduces a new approach to incorporate semantic knowledge into latent matching models, addressing data sparsity issues for tail queries.

Findings

Semantic knowledge improves matching accuracy.

Model performs well on tail queries.

Significant accuracy gains demonstrated on real datasets.

Abstract

The relevance between a query and a document in search can be represented as matching degree between the two objects. Latent space models have been proven to be effective for the task, which are often trained with click-through data. One technical challenge with the approach is that it is hard to train a model for tail queries and tail documents for which there are not enough clicks. In this paper, we propose to address the challenge by learning a latent matching model, using not only click-through data but also semantic knowledge. The semantic knowledge can be categories of queries and documents as well as synonyms of words, manually or automatically created. Specifically, we incorporate semantic knowledge into the objective function by including regularization terms. We develop two methods to solve the learning task on the basis of coordinate descent and gradient descent respectively, which can be employed in different settings. Experimental results on two datasets from an app search engine demonstrate that our model can make effective use of semantic knowledge, and thus can significantly enhance the accuracies of latent matching models, particularly for tail queries.