Semantic Matching in Search

Relevance is the most important factor to meet users' satisfaction in search and the success of a search engine heavily depends on its performance on relevance. It has been observed that most of the dissatisfaction cases in search relevance are due to term mismatch between queries and documents (e.g., query "ny times" does not match well with document only containing "New York Times"), because term matching, i.e., the bag-of-words approach, still functions as the main mechanism of modern search engines. It is no exaggeration to say, therefore, that mismatch between query and document poses the most critical challenge in search. Recently researchers have spent significant effort to address the problem. The major approach is to conduct semantic matching, i.e., perform more query and document understanding, and perform better matching between enriched query and document representations. With the availability of a large amount of log data and advanced machine learning techniques, this becomes more feasible and significant progress has been made recently. This book provides a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly web search. It focuses on the fundamental problems, as well as the state-of-the-art solutions for query document matching on form aspect, phrase aspect, word sense aspect, topic aspect, and structure aspect. The ideas and solutions explained may motivate industrial practitioners to turn the research results into products. Matching between query and document is not limited to search and similar problems can be found in question answering, online advertisement, cross language information retrieval, machine translation, recommendation system, link prediction, image annotation, drug design, and other applications, as the general task of matching between objects from two different spaces. The technologies introduced here can be generalized into more general machine learning techniques, which the authors call learning to match.