Toward the Extension and Enhanced Representation for Ambiguous Query With Search Heterogeneous Graph Learning

In an online search, users often input an ambiguous short query to search engines, which leads to search engines being unable to accurately understand the true users' query intent. Thus, enhancing the users' query intent is necessary. Traditional methods of guessing and inferring user intentions are based on either personal past search data, or the group's search history data. The former faces the cold start problem for new users due to the lack of search history data, while the latter cannot accurately get the intent of new search requests due to different users having different intentions even for the same search query. To solve the above issues and to enhance the representation of search requests by adding some query keywords, we construct a user-query-document search heterogeneous graph with users' search history data of their friend networks, which can express the behavioral features and interrelationships of searches. To facilitate the enhanced representation of a query intent, we present TAHAN, a type-aware heterogeneous graph attention network (GAT) model. Extensive experiments on real-world datasets show that our method not only outperforms the state-of-the-art models, but also achieves superior performance in addressing the data sparsity and cold-start problems.