chore: update confs

Author: actions-user

papers/cikm/cikm2023.md

@@ -42,7 +42,7 @@
 |[A Conversational Search Framework for Multimedia Archives](https://doi.org/10.1007/978-3-031-56069-9_25)|Anastasia Potyagalova, Gareth J. F. Jones||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=A+Conversational+Search+Framework+for+Multimedia+Archives)|0|
 |[Effective and Efficient Transformer Models for Sequential Recommendation](https://doi.org/10.1007/978-3-031-56069-9_39)|Aleksandr V. Petrov||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Effective+and+Efficient+Transformer+Models+for+Sequential+Recommendation)|0|
 |[Quantum Computing for Information Retrieval and Recommender Systems](https://doi.org/10.1007/978-3-031-56069-9_47)|Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Quantum+Computing+for+Information+Retrieval+and+Recommender+Systems)|0|
-|[Transformers for Sequential Recommendation](https://doi.org/10.1007/978-3-031-56069-9_49)|Aleksandr V. Petrov, Craig Macdonald|Ocean University of China, Qingdao, China; Wuhan University, Wuhan, China; University of Hong Kong, Hong Kong, China; National University of Singapore, Singapore, Singapore|Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced T ransformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally,we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of- the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.|学习动态用户偏好已经成为许多在线平台(如视频分享网站、电子商务系统)提供连续推荐的一个越来越重要的组成部分。以往的研究基于多种体系结构，如递归神经网络和自我注意机制，对用户交互序列上的项目-项目转换进行了大量的研究。最近出现的图形神经网络也可以作为有用的骨干模型，以捕获项目依赖的顺序推荐场景。尽管现有的方法很有效，但是现有的方法都集中在单一交互类型的项目序列表示上，因此仅限于捕获用户和项目之间的动态异构关系结构(例如，页面查看、添加到收藏夹、购买)。为了应对这一挑战，我们设计了一个多行为超图增强型 T 变换器框架(MBHT)来捕获短期和长期的跨类型行为依赖。具体而言，多尺度变压器配备低级自注意，以从细粒度和粗粒度级别联合编码行为感知的序列模式。此外，我们将全局多行为依赖引入到超图神经结构中，以自定义的方式获取层次化的长期项目相关性。实验结果表明，我们的 MBHT 优于不同设置的各种最先进的推荐解决方案。进一步的消融研究验证了我们的模型设计的有效性和新的 MBHT 框架的好处。我们的实施代码在以下 https://github.com/yuh-yang/mbht-kdd22发布:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transformers+for+Sequential+Recommendation)|0|
+|[Transformers for Sequential Recommendation](https://doi.org/10.1007/978-3-031-56069-9_49)|Aleksandr V. Petrov, Craig Macdonald|University of Hong Kong, Hong Kong, China; National University of Singapore, Singapore, Singapore; Wuhan University, Wuhan, China; Ocean University of China, Qingdao, China|Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced T ransformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally,we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of- the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.|学习动态用户偏好已经成为许多在线平台(如视频分享网站、电子商务系统)提供连续推荐的一个越来越重要的组成部分。以往的研究基于多种体系结构，如递归神经网络和自我注意机制，对用户交互序列上的项目-项目转换进行了大量的研究。最近出现的图形神经网络也可以作为有用的骨干模型，以捕获项目依赖的顺序推荐场景。尽管现有的方法很有效，但是现有的方法都集中在单一交互类型的项目序列表示上，因此仅限于捕获用户和项目之间的动态异构关系结构(例如，页面查看、添加到收藏夹、购买)。为了应对这一挑战，我们设计了一个多行为超图增强型 T 变换器框架(MBHT)来捕获短期和长期的跨类型行为依赖。具体而言，多尺度变压器配备低级自注意，以从细粒度和粗粒度级别联合编码行为感知的序列模式。此外，我们将全局多行为依赖引入到超图神经结构中，以自定义的方式获取层次化的长期项目相关性。实验结果表明，我们的 MBHT 优于不同设置的各种最先进的推荐解决方案。进一步的消融研究验证了我们的模型设计的有效性和新的 MBHT 框架的好处。我们的实施代码在以下 https://github.com/yuh-yang/mbht-kdd22发布:。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Transformers+for+Sequential+Recommendation)|0|
 |[Context-Aware Query Term Difficulty Estimation for Performance Prediction](https://doi.org/10.1007/978-3-031-56066-8_4)|Abbas Saleminezhad, Negar Arabzadeh, Soosan Beheshti, Ebrahim Bagheri||||[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Context-Aware+Query+Term+Difficulty+Estimation+for+Performance+Prediction)|0|
 |[Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects](https://doi.org/10.1007/978-3-031-56066-8_30)|Federico Maria Cau, Nava Tintarev||Opinionated users often seek information that aligns with their preexisting beliefs while dismissing contradictory evidence due to confirmation bias. This conduct hinders their ability to consider alternative stances when searching the web. Despite this, few studies have analyzed how the diversification of search results on disputed topics influences the search behavior of highly opinionated users. To this end, we present a preregistered user study (n = 257) investigating whether different levels (low and high) of bias metrics and search results presentation (with or without AI-predicted stances labels) can affect the stance diversity consumption and search behavior of opinionated users on three debated topics (i.e., atheism, intellectual property rights, and school uniforms). Our results show that exposing participants to (counter-attitudinally) biased search results increases their consumption of attitude-opposing content, but we also found that bias was associated with a trend toward overall fewer interactions within the search page. We also found that 19 any search results. When we removed these participants in a post-hoc analysis, we found that stance labels increased the diversity of stances consumed by users, particularly when the search results were biased. Our findings highlight the need for future research to explore distinct search scenario settings to gain insight into opinionated users' behavior.|固执己见的用户往往寻求与他们先前存在的信念相一致的信息，而由于确认偏见而排除相互矛盾的证据。这种行为妨碍了他们在搜索网页时考虑其他立场的能力。尽管如此，很少有研究分析有争议话题的搜索结果的多样化如何影响高度固执己见的用户的搜索行为。为此，我们提出了一项预先注册的用户研究(n = 257) ，调查不同水平(低和高)的偏倚指标和搜索结果表示(有或没有 AI 预测的立场标签)是否会影响立场多样性消费和搜索行为有意见的用户在三个有争议的话题(即无神论，知识产权和校服)。我们的研究结果显示，将参与者暴露在(反态度的)有偏见的搜索结果中，会增加他们对与态度相反的内容的消费，但是我们也发现，偏见与搜索页面内的整体互动减少的趋势有关。我们还发现19任何搜索结果。当我们在一个事后比较中移除这些参与者时，我们发现立场标签增加了用户使用的立场的多样性，特别是当搜索结果有偏见时。我们的研究结果强调了未来研究探索不同搜索场景设置的必要性，以深入了解固执己见的用户的行为。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Navigating+the+Thin+Line:+Examining+User+Behavior+in+Search+to+Detect+Engagement+and+Backfire+Effects)|0|
 |[Measuring Bias in a Ranked List Using Term-Based Representations](https://doi.org/10.1007/978-3-031-56069-9_1)|Amin Abolghasemi, Leif Azzopardi, Arian Askari, Maarten de Rijke, Suzan Verberne||In most recent studies, gender bias in document ranking is evaluated with the NFaiRR metric, which measures bias in a ranked list based on an aggregation over the unbiasedness scores of each ranked document. This perspective in measuring the bias of a ranked list has a key limitation: individual documents of a ranked list might be biased while the ranked list as a whole balances the groups' representations. To address this issue, we propose a novel metric called TExFAIR (term exposure-based fairness), which is based on two new extensions to a generic fairness evaluation framework, attention-weighted ranking fairness (AWRF). TExFAIR assesses fairness based on the term-based representation of groups in a ranked list: (i) an explicit definition of associating documents to groups based on probabilistic term-level associations, and (ii) a rank-biased discounting factor (RBDF) for counting non-representative documents towards the measurement of the fairness of a ranked list. We assess TExFAIR on the task of measuring gender bias in passage ranking, and study the relationship between TExFAIR and NFaiRR. Our experiments show that there is no strong correlation between TExFAIR and NFaiRR, which indicates that TExFAIR measures a different dimension of fairness than NFaiRR. With TExFAIR, we extend the AWRF framework to allow for the evaluation of fairness in settings with term-based representations of groups in documents in a ranked list.|在最近的大多数研究中，文档排名中的性别偏见是通过 NFaiRR 度量来评估的，该度量基于每个排名文档的无偏评分的聚合来衡量排名列表中的偏见。这种测量排名表偏差的视角有一个关键的局限性: 排名表的个别文档可能有偏差，而排名表作为一个整体平衡各组的表示。为了解决这个问题，我们提出了一种新的度量方法 TExFAIR (术语暴露公平性) ，它基于通用公平性评估框架的两个新的扩展，即注意力加权排序公平性(AWRF)。TExFAIR 基于排名列表中基于术语的群体表示来评估公平性: (i)基于概率术语水平关联的关联文档与群体的明确定义，以及(ii)用于计数非代表性文档的排名折扣因子(RBDF)对排名列表的公平性进行测量。我们通过测量文章排序中的性别偏见来评估 TExFAIR，并研究 TExFAIR 和 NFaiRR 之间的关系。我们的实验表明，TExFAIR 和 NFaiRR 之间没有很强的相关性，这表明 TExFAIR 测量的公平性维度不同于 NFaiRR。通过 TExFAIR，我们扩展了 AWRF 框架，允许在排名列表中的文档中使用基于术语的群组表示来评估设置中的公平性。|[code](https://paperswithcode.com/search?q_meta=&q_type=&q=Measuring+Bias+in+a+Ranked+List+Using+Term-Based+Representations)|0|