Universal query embeddings that accurately capture the semantic meaning of search queries are crucial for supporting a range of query understanding (QU) tasks within enterprises.However, current embedding approaches often struggle to effectively represent queries due to the shortness of search queries and their tendency for surface-level variations.We propose a user-behavior-driven contrastive learning approach which directly aligns embeddings according to user intent.This approach uses intent-aligned query pairs as positive examples, derived from two types of real-world user interactions: (1) clickthrough data, in which queries leading to clicks on the same URLs are assumed to share the same intent, and (2) session data, in which queries within the same user session are considered to share intent.By incorporating these query pairs into a robust contrastive learning framework, we can construct query embedding models that align with user intent while minimizing reliance on surface-level lexical similarities.Evaluations on real-world QU tasks demonstrated that these models substantially outperformed state-of-the-art text embedding models such as mE5 and SimCSE.Our models have been deployed in our search engine to support QU technologies.

Citation count prediction is the task of predicting the future citation counts of academic papers, which is particularly useful for estimating the future impacts of an ever-growing number of academic papers. Although there have been many studies on citation count prediction, they are not applicable to predicting the citation counts of newly published papers, because they assume the availability of future citation counts for papers that have not had enough time pass since publication. In this paper, we first identify problems in the settings of existing studies and introduce a realistic citation count prediction task that strictly uses information available at the time of a target paper’s publication. For realistic citation count prediction, we then propose two methods to leverage the citation counts of papers shortly after publication. Through experiments using papers collected from arXiv and bioRxiv, we demonstrate that our methods considerably improve the performance of citation count prediction for newly published papers in a realistic setting.