Nowadays, cognitive service provides more interactive way to understand users' requirements via human-machine conversation. In other words, it has to capture users' requirements from their utterance and respond them with the relevant and suitable service resources. To this end, two phases must be applied: I.Sequence planning and Real-time detection of user requirement, II.Service resource selection and Response generation. The existing works ignore the potential connection between these two phases. To model their connection, Two-Phase Requirement Elicitation Method is proposed. For the phase I, this paper proposes a user requirement elicitation framework (URef) to plan a potential requirement sequence grounded on user profile and personal knowledge base before the conversation. In addition, it can also predict user's true requirement and judge whether the requirement is completed based on the user's utterance during the conversation. For the phase II, this paper proposes a response generation model based on attention, SaRSNet. It can select the appropriate resource (i.e. knowledge triple) in line with the requirement predicted by URef, and then generates a suitable response for recommendation. The experimental results on the open dataset \emph{DuRecDial} have been significantly improved compared to the baseline, which proves the effectiveness of the proposed methods.
Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search
College of Computer Science, Sichuan University（四川大学计算机学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
Cognitive Biases in Large Language Models for News Recommendation
University of Amsterdam（阿姆斯特丹大学）；Shandong University（山东大学）；Leiden University（莱顿大学）；Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核子研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Institutes of Physical Science and Information Technology, Anhui University（安徽大学物理与信息技术研究院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Yandex；SBDA Group；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Institute of Numerical Mathematics, Russian Academy of Sciences（俄罗斯科学院数值数学研究所）
AI, Help Me Think$\unicode{x2014}$but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support
Microsoft Research Cambridge（微软研究院剑桥分部）；University College London（伦敦大学学院）；fortiss GmbH, Research Institute of the Free State of Bavaria（巴伐利亚自由州fortiss GmbH研究机构）；LMU Munich（慕尼黑大学）；Politecnico di Milano（米兰理工大学）
Vector Space Model as Cognitive Space for Text Classification
Center for Computational Engineering and Networking（计算工程与网络中心）；Amrita School of Engineering, Coimbatore（科钦阿姆利塔工程学院）；Amrita Vishwa Vidyapeetham, Amrita University, India（印度阿姆利塔大学阿姆利塔大学）
