A Language Model Approach to Capture Commercial Intent and Information Relevance for Sponsored Search

摘要

A fundamental task of sponsored search is how to find the best match between web search queries and textual advertisements. To address this problem, we explicitly characterize the criteria for an advertisement to be a 'good match' to a query from two aspects (it should be relevant with the query from information perspective, and it should be able to capture and satisfy the commercial intent in the query). Correspondingly, we introduce in this paper a mixture language model of two parts: a commercial model which characterizes language bias of commercial intent leveraging on users' clicks on advertisements, and an informational model which is a traditional language model with consideration of the entropy of each word to capture informational relevance. We then introduce a regularized expectation-maximization (EM) algorithm model for parameters estimation, and integrate query commercial intent into the scoring function to boost overall click efficiency. Empirical evaluation shows that our model achieves better performance as compared to a well tuned classical language model and deliberated TFIDF-pLSI model (6% and 5% precision improvement at our operating point in production environment of 30% recall, and 5.3% and 6.3% AUC improvement), and performs superior to the KL Divergence language model for tail queries (0.5% nDCG improvement). Live traffic test shows over 2% CTR lift and 2.5% RPS lift as well.