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The relevance between a query and a document in search can be represented asmatching degree between the two objects. Latent space models have been provento 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 modelfor tail queries and tail documents for which there are not enough clicks. Inthis paper, we propose to address the challenge by learning a latent matchingmodel, using not only click-through data but also semantic knowledge. Thesemantic knowledge can be categories of queries and documents as well assynonyms of words, manually or automatically created. Specifically, weincorporate semantic knowledge into the objective function by includingregularization terms. We develop two methods to solve the learning task on thebasis of coordinate descent and gradient descent respectively, which can beemployed in different settings. Experimental results on two datasets from anapp search engine demonstrate that our model can make effective use of semanticknowledge, and thus can significantly enhance the accuracies of latent matchingmodels, particularly for tail queries.

Incorporating Semantic Knowledge into Latent Matching Model in Search