Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This approach translates the multilingual search problem into a particular task of search diversification. Furthermore, the standard intent-aware approach could be adopted to build a diversified metric for multilingual search on the basis of a classical IR metric such as ERR. The intent-aware approach estimates user satisfaction under a user behavior model. We show however that the underlying user behavior models is not realistic in the multilingual case, and the produced intent-aware metric do not appropriately estimate the user satisfaction. We develop a novel approach to build intent-aware user behavior models, which overcome these limitations and convert to quality metrics that better correlate with standard online metrics of user satisfaction.
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Alibaba Group（阿里巴巴集团）；Ant Financial Services Group（蚂蚁金融服务集团）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Rutgers University（罗格斯大学）
Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
School of Information Science and Engineering, Yanshan University（燕山大学信息科学与工程学院）；School of Information Science and Engineering, Xinjiang College Of Science & Technology（新疆科学技术学院信息科学与工程学院）
Anatomy of a Decision: Uncertainty-aware Hierarchical Intent Learning via Flow Matching for Multimodal Recommendation
Sydney Smart Technology College, Northeastern University（悉尼智能科技学院，东北大学）；School of Computer and Communication Engineering and Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao（秦皇岛东北大学计算机与通信工程学院及河北省海洋感知网络与数据处理重点实验室）；Shijiazhuang Innovation Research Institute of Northeastern University（东北大学石家庄创新研究院）
