  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.
