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SIGIR-AP 2024: International ACM SIGIR Conference on Information Retrieval in the Asia Pacific

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SIGIR-AP
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截稿日期:
2024-07-01
通知日期:
2024-09-23
会议日期:
2024-12-09
会议地点:
Tokyo, Japan
届数:
2
浏览: 7877   关注: 0   参加: 0

会伴指数 (CP-I)

43.5 / 100
全站第 3,555 名 / 共 5,682 个会议 · 前 63%

数据挖掘与数据库 第 208 / 337

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
37
社区关注 (10%)
10
资料公开度 (15%)
50

用到的输入: 有据可查的届次：3 · 过去 24 个月打开过本页的研究者：3 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-19

征稿

SIGIR-AP 2024 (International ACM SIGIR Conference on Information Retrieval in the Asia Pacific) is an academic conference held in Tokyo, Japan on 2024-12-09. The paper submission deadline is 2024-07-01. Acceptance notifications are sent on 2024-09-23.

TOPICS OF INTEREST The topics of SIGIR-AP are the same as those of SIGIR. Relevant areas include but are not limited to: Search and Ranking: Research on core IR concepts, principles, and algorithms: Queries and query analysis Web search Retrieval model Ranking model Theoretical models and foundations of information retrieval and access System, Efficiency, and Scalability: Research on system aspects related to the efficiency and scalability of IR systems: Efficient and scalable indexing Energy efficiency and green computing for IR Search engine architecture Distributed search, edge IR Recommender Systems: Research on recommender systems: Filtering and recommendation Cross-domain recommendation Novel approaches to recommendation Interactive and conversational recommendation Evaluation of recommender systems Other theoretical models and foundations of recommender systems Machine Learning and NLP for IR: Research that bridges ML, NLP, and IR: Core ML applied to IR Large Language Models for IR Retrieval Augmented Machine Learning Question answering Conversational IR: Research on developing intelligent IR systems that support interactive conversations with users: End-to-end conversational IR models and optimization Modularized IR techniques (e.g., query understanding, user modeling, intent prediction, context and discourse management, reranking, and results presentation) Session based search or recommendation, user engagement Conversational question answering Spoken language interfaces Intelligent personal assistants and agents Humans and Interfaces: Research on user-centric aspects of IR, including user interfaces, behavior modeling, privacy, and interactive systems, such as: Mining and modeling users User simulation Interactive search Social search Collaborative search Information security User studies Other theoretical models and foundations of user-centric IR Evaluation: Research on the measurement and evaluation of IR systems, such as: User-centered evaluation System-centered evaluation Beyond Cranfield Beyond labels Beyond effectiveness Novel methodology for evaluation Fairness, Accountability, Transparency, Ethics, and Explainability (FATE) in IR: Research on FATE aspects and biases in IR: Fairness, accountability, transparency Ethics, economics, and politics Two-sided search and recommendation scenarios Personal safety Multimodal IR: Research that bridges the domain of multimedia and IR, such as: Multimedia search Multimedia recommendation Multimodal for IR Domain-specific Applications: Research on domain-specific IR problems and challenges, such as: Local and mobile search Social search Search in structured data Education Legal Health Other applications and domains Other IR Topics: Research on any IR-related research that does not belong to any of the areas above, such as: Explicit semantics Knowledge acquisition Knowledge representation and reasoning Document representation and content analysis
由 Dou Sun 最后更新于

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