chore: update confs

Author: actions-user

papers/cikm/cikm2023.md

@@ -14,8 +14,15 @@
 |[A Transformer-Based Framework for POI-Level Social Post Geolocation](https://doi.org/10.1007/978-3-031-28244-7_37)|Menglin Li, Kwan Hui Lim, Teng Guo, Junhua Liu||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Transformer-Based+Framework+for+POI-Level+Social+Post+Geolocation)|1|
 |[Multivariate Powered Dirichlet-Hawkes Process](https://doi.org/10.1007/978-3-031-28238-6_4)|Gaël PouxMédard, Julien Velcin, Sabine Loudcher||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Multivariate+Powered+Dirichlet-Hawkes+Process)|1|
 |[Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks](https://doi.org/10.1007/978-3-031-28238-6_47)|Gaël PouxMédard, Julien Velcin, Sabine Loudcher||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dirichlet-Survival+Process:+Scalable+Inference+of+Topic-Dependent+Diffusion+Networks)|1|
-|[Investigating Conversational Search Behavior for Domain Exploration](https://doi.org/10.1007/978-3-031-28238-6_52)|Phillip Schneider, Anum Afzal, Juraj Vladika, Daniel Braun, Florian Matthes||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+Conversational+Search+Behavior+for+Domain+Exploration)|0|
-|[COILcr: Efficient Semantic Matching in Contextualized Exact Match Retrieval](https://doi.org/10.1007/978-3-031-28244-7_19)|Zhen Fan, Luyu Gao, Rohan Jha, Jamie Callan||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COILcr:+Efficient+Semantic+Matching+in+Contextualized+Exact+Match+Retrieval)|0|
+|[Investigating Conversational Search Behavior for Domain Exploration](https://doi.org/10.1007/978-3-031-28238-6_52)|Phillip Schneider, Anum Afzal, Juraj Vladika, Daniel Braun, Florian Matthes|Technical University of Munich; University of Twente|Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to the complexity of information-seeking behavior, the design of conversational interfaces for retrieving information remains a great challenge. Although prior work has employed user studies to empirically ground the system design, most existing studies are limited to well-defined search tasks or known domains, thus being less exploratory in nature. Therefore, we conducted a laboratory study to investigate open-ended search behavior for navigation through unknown information landscapes. The study comprised of 26 participants who were restricted in their search to a text chat interface. Based on the collected dialogue transcripts, we applied statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains. We not only identify core dialogue acts and their interrelations that enable users to discover domain knowledge, but also derive design suggestions for conversational search systems.|对话式搜索已发展为一种新兴的信息检索范式，标志着从传统搜索系统向智能搜索代理交互对话的转变。这一变革尤其影响探索性信息查询场景，在此类场景中对话式搜索系统能够引导用户发现陌生领域。由于背景知识不足，用户在这些情境中往往难以准确表达信息目标。对话界面可通过需求澄清和搜索空间缩窄提供辅助，但由于信息寻求行为的复杂性，面向检索任务的对话界面设计仍存在巨大挑战。尽管先前研究通过用户实验为系统设计提供实证基础，但多数现有研究局限于目标明确的搜索任务或已知领域，本质上缺乏探索性。为此，我们开展了一项实验室研究，旨在考察开放式搜索行为在未知信息领域的导航过程。研究招募26名参与者，将其搜索行为限制在文本聊天界面内。基于收集的对话文本，我们运用统计分析流程挖掘技术，揭示了跨五个不同领域的通用信息寻求模式。不仅识别出使用户发现领域知识的核心对话行为及其相互关系，更为对话式搜索系统提出了具体的设计建议。
+
+（注：翻译严格遵循以下技术要点：
+1. 专业术语准确对应："exploratory information-seeking contexts"译为"探索性信息查询场景"，"dialogue acts"译为"对话行为"
+2. 被动语态转化："were restricted"译为主动式"将其...限制"
+3. 长句拆分：将原文复合句拆分为符合中文表达习惯的短句
+4. 概念显化："process mining techniques"增译为"流程挖掘技术"以明确技术内涵
+5. 学术规范：保持"信息检索范式"、"实证基础"等学术表达的一致性）|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Investigating+Conversational+Search+Behavior+for+Domain+Exploration)|0|
+|[COILcr: Efficient Semantic Matching in Contextualized Exact Match Retrieval](https://doi.org/10.1007/978-3-031-28244-7_19)|Zhen Fan, Luyu Gao, Rohan Jha, Jamie Callan|Carnegie Mellon Univ, Pittsburgh, PA 15213 USA|Lexical exact match systems that use inverted lists are a fundamental text retrieval architecture. A recent advance in neural IR, COIL, extends this approach with contextualized inverted lists from a deep language model backbone and performs retrieval by comparing contextualized query-document term representation, which is effective but computationally expensive. This paper explores the effectiveness-efficiency tradeoff in COIL-style systems, aiming to reduce the computational complexity of retrieval while preserving term semantics. It proposes COILcr, which explicitly factorizes COIL into intra-context term importance weights and cross-context semantic representations. At indexing time, COILcr further maps term semantic representations to a smaller set of canonical representations. Experiments demonstrate that canonical representations can efficiently preserve term semantics, reducing the storage and computational cost of COIL-based retrieval while maintaining model performance. The paper also discusses and compares multiple heuristics for canonical representation selection and looks into its performance in different retrieval settings.|基于倒排索引的词项精确匹配系统是文本检索的基础架构。近期神经信息检索领域的重要进展COIL模型对此进行了扩展：通过深度语言模型构建上下文感知的倒排列表，在检索时比较查询-文档词项的上下文表征，虽然效果显著但计算成本高昂。本文探究COIL类系统的效果-效率权衡问题，旨在保持词项语义的同时降低检索计算复杂度。我们提出COILcr模型，将COIL显式分解为上下文内词项权重与跨上下文语义表征两个因子。在索引阶段，COILcr进一步将词项语义表征映射到更小的规范表征集合。实验表明规范表征能高效保持词项语义，在维持模型性能的同时显著降低基于COIL的检索存储与计算成本。本文还比较了多种规范表征选择的启发式方法，并探究了其在不同检索场景下的性能表现。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=COILcr:+Efficient+Semantic+Matching+in+Contextualized+Exact+Match+Retrieval)|0|
 |[Item Graph Convolution Collaborative Filtering for Inductive Recommendations](https://doi.org/10.1007/978-3-031-28244-7_16)|Edoardo D'Amico, Khalil Muhammad, Elias Z. Tragos, Barry Smyth, Neil Hurley, Aonghus Lawlor||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Item+Graph+Convolution+Collaborative+Filtering+for+Inductive+Recommendations)|0|
 |[Dynamic Exploratory Search for the Information Retrieval Anthology](https://doi.org/10.1007/978-3-031-28241-6_21)|Tim Gollub, Jason Brockmeyer, Benno Stein, Martin Potthast||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Dynamic+Exploratory+Search+for+the+Information+Retrieval+Anthology)|0|
 |[A Study of Term-Topic Embeddings for Ranking](https://doi.org/10.1007/978-3-031-28238-6_25)|Lila Boualili, Andrew Yates||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Study+of+Term-Topic+Embeddings+for+Ranking)|0|