Incorporating Semantic Knowledge into Latent Matching Model in Search
This work addresses a specific problem in search engines for improving relevance matching, particularly for less common queries, and is incremental as it builds on existing latent space models.
The paper tackles the challenge of training latent matching models for tail queries and documents with insufficient click-through data by incorporating semantic knowledge like categories and synonyms into the objective function via regularization, resulting in significantly enhanced accuracies for tail queries on two app search datasets.
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.