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Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献

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MVL: Multi-View Learning for News Recommendation MVL:新闻推荐的多视角学习
Santosh T.Y.S.S, Avirup Saha, Niloy Ganguly
In this paper, we propose a Multi-View Learning (MVL) framework for news recommendation which uses both the content view and the user-news interaction graph view. In the content view, we use a news encoder to learn news representations from different information like titles, bodies and categories. We obtain representation of user from his/her browsed news conditioned on the candidate news article to be recommended. In the graph-view, we propose to use a graph neural network to capture the user-news, user-user and news-news relatedness in the user-news bipartite graphs by modeling the interactions between different users and news. In addition, we propose to incorporate attention mechanism into the graph neural network to model the importance of these interactions for more informative representation learning of user and news. Experiments on a real world dataset validate the effectiveness of MVL.
本文提出了一种基于内容视图和用户新闻交互图视图的新闻推荐多视图学习(MVL)框架。在内容视图中,我们使用新闻编码器从标题、正文和类别等不同的信息中学习新闻表示。我们根据要推荐的候选新闻文章,从他/她浏览的新闻中获得用户的表示。在图视图中,我们建议使用图神经网络通过建模不同用户与新闻之间的交互来捕获用户-新闻、用户-用户和新闻-新闻的二部图相关性。此外,我们建议将注意力机制整合到图神经网络中,以模拟这些交互对用户和新闻更有信息表示学习的重要性。在实际数据集上的实验验证了MVL的有效性。
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引用次数: 21
Sentiment-guided Sequential Recommendation 情绪导向的顺序推荐
Lin Zheng, Naicheng Guo, Weihao Chen, Jin Yu, Dazhi Jiang
The existing sequential recommendation methods focus on modeling the temporal relationships of user behaviors and are good at using additional item information to improve performance. However, these methods rarely consider the influences of users' sequential subjective sentiments on their behaviors---and sometimes the temporal changes in human sentiment patterns plays a decisive role in users' final preferences. To investigate the influence of temporal sentiments on user preferences, we propose generating preferences by guiding user behavior through sequential sentiments. Specifically, we design a dual-channel fusion mechanism. The main channel consists of sentiment-guided attention to match and guide sequential user behavior, and the secondary channel consists of sparse sentiment attention to assist in preference generation. In the experiments, we demonstrate the effectiveness of these two sentiment modeling mechanisms through ablation studies. Our approach outperforms current state-of-the-art sequential recommendation methods that incorporate sentiment factors.
现有的顺序推荐方法侧重于对用户行为的时间关系进行建模,并善于利用附加的项目信息来提高性能。然而,这些方法很少考虑用户连续主观情绪对其行为的影响——有时人类情绪模式的时间变化对用户的最终偏好起决定性作用。为了研究时间情绪对用户偏好的影响,我们提出通过顺序情绪引导用户行为来生成偏好。具体来说,我们设计了一个双通道融合机制。主通道由情感引导的注意组成,用于匹配和引导顺序用户行为,次通道由稀疏的情感注意组成,以帮助偏好生成。在实验中,我们通过消融研究证明了这两种情感建模机制的有效性。我们的方法优于目前最先进的包含情绪因素的顺序推荐方法。
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引用次数: 20
Learning Discriminative Joint Embeddings for Efficient Face and Voice Association 人脸与语音高效关联的学习判别联合嵌入
Rui Wang, Xin Liu, Y. Cheung, Kai Cheng, Nannan Wang, Wentao Fan
Many cognitive researches have shown the natural possibility of face-voice association, and such potential association has attracted much attention in biometric cross-modal retrieval domain. Nevertheless, the existing methods often fail to explicitly learn the common embeddings for challenging face-voice association tasks. In this paper, we present to learn discriminative joint embedding for face-voice association, which can seamlessly train the face subnetwork and voice subnetwork to learn their high-level semantic features, while correlating them to be compared directly and efficiently. Within the proposed approach, we introduce bi-directional ranking constraint, identity constraint and center constraint to learn the joint face-voice embedding, and adopt bi-directional training strategy to train the deep correlated face-voice model. Meanwhile, an online hard negative mining technique is utilized to discriminatively construct hard triplets in a mini-batch manner, featuring on speeding up the learning process. Accordingly, the proposed approach is adaptive to benefit various face-voice association tasks, including cross-modal verification, 1:2 matching, 1:N matching, and retrieval scenarios. Extensive experiments have shown its improved performances in comparison with the state-of-the-art ones.
许多认知研究已经证明了人脸-语音关联的自然可能性,这种潜在关联在生物识别跨模态检索领域受到了广泛关注。然而,现有的方法往往不能明确地学习具有挑战性的人脸-语音关联任务的共同嵌入。本文提出了一种用于人脸-语音关联的学习判别联合嵌入方法,该方法可以无缝地训练人脸子网和语音子网学习它们的高级语义特征,并将它们关联起来进行直接有效的比较。在该方法中,我们引入双向排名约束、身份约束和中心约束来学习人脸-语音联合嵌入,并采用双向训练策略训练深度相关人脸-语音模型。同时,利用在线硬负挖掘技术,以小批量的方式判别构建硬三元组,加快了学习过程。因此,该方法适用于各种人脸语音关联任务,包括跨模态验证、1:2匹配、1:N匹配和检索场景。大量的实验表明,与最先进的产品相比,它的性能有所提高。
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引用次数: 10
Domain Adaptation with Reconstruction for Disaster Tweet Classification 基于重构的领域自适应灾害推文分类
Xukun Li, Doina Caragea
Identifying critical information in real time in the beginning of a disaster is a challenging but important task. This task has been recently addressed using domain adaptation approaches, which eliminate the need for target labeled data, and can thus accelerate the process of identifying useful information. We propose to investigate the effectiveness of the Domain Reconstruction Classification Network (DRCN) approach on disaster tweets. DRCN adapts information from target data by reconstructing it with an autoencoder. Experimental results using a sequence-to-sequence autoencodershow that the DRCN approach can improve the performance of both supervised and domain adaptation baseline models.
在灾难开始时实时识别关键信息是一项具有挑战性但又很重要的任务。这个任务最近已经通过使用领域适应方法来解决,该方法消除了对目标标记数据的需要,从而可以加速识别有用信息的过程。我们建议研究领域重建分类网络(DRCN)方法在灾难推文上的有效性。DRCN通过自编码器重构目标数据来适应信息。使用序列到序列自编码器的实验结果表明,DRCN方法可以提高监督基线模型和领域自适应基线模型的性能。
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引用次数: 11
Fair Classification with Counterfactual Learning 基于反事实学习的公平分类
M. Tavakol
Recent advances in machine learning have led to emerging new approaches to deal with different kinds of biases that exist in the data. On the one hand, counterfactual learning copes with biases in the policy used for sampling (or logging) the data in order to evaluate and learn new policies. On the other hand, fairness-aware learning aims at learning fair models to avoid discrimination against certain individuals or groups. In this paper, we design a counterfactual framework to model fairness-aware learning which benefits from counterfactual reasoning to achieve more fair decision support systems. We utilize a definition of fairness to determine the bandit feedback in the counterfactual setting that learns a classification strategy from the offline data, and balances classification performance versus fairness measure. In the experiments, we demonstrate that a counterfactual setting can be perfectly exerted to learn fair models with competitive results compared to a well-known baseline system.
机器学习的最新进展导致了处理数据中存在的不同类型偏见的新方法的出现。一方面,反事实学习处理用于采样(或记录)数据的策略中的偏差,以便评估和学习新策略。另一方面,公平意识学习旨在学习公平模式,以避免对某些个人或群体的歧视。在本文中,我们设计了一个反事实框架来模拟公平感知学习,这种学习受益于反事实推理来实现更公平的决策支持系统。我们利用公平性的定义来确定反事实设置中的强盗反馈,该设置从离线数据中学习分类策略,并平衡分类性能与公平性度量。在实验中,我们证明了与已知的基线系统相比,反事实设置可以完美地用于学习具有竞争性结果的公平模型。
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引用次数: 6
An Intent-guided Collaborative Machine for Session-based Recommendation 基于会话推荐的意图引导协同机器
Zhiqiang Pan, Fei Cai, Yanxiang Ling, M. de Rijke
Session-based recommendation produces item predictions mainly based on anonymous sessions. Previous studies have leveraged collaborative information from neighbor sessions to boost the recommendation accuracy for a given ongoing session. Previous work often selects the most recent sessions as candidate neighbors, thereby failing to identify the most related neighbors to obtain an effective neighbor representation. In addition, few existing methods simultaneously consider the sequential signal and the most recent interest in an ongoing session. In this paper, we introduce an Intent-guided Collaborative Machine for Session-based Recommendation (ICM-SR). ICM-SR encodes an ongoing session by leveraging the prior sequential items and the last item to generate an accurate session representation, which is then used to produce initial item predictions as intent. After that, we design an intent-guided neighbor detector to locate the correct neighbor sessions. Finally, the representations of the current session and the neighbor sessions are adaptively combined by a gated fusion layer to produce the final item recommendations. Experiments conducted on two public benchmark datasets show that ICM-SR achieves a significant improvement in terms of Recall and MRR over the state-of-the-art baselines.
基于会话的推荐主要基于匿名会话产生项目预测。以前的研究利用来自邻居会话的协作信息来提高给定正在进行的会话的推荐准确性。以前的工作通常选择最近的会话作为候选邻居,因此无法识别最相关的邻居以获得有效的邻居表示。此外,很少有现有的方法同时考虑正在进行的会话中的顺序信号和最近的兴趣。本文介绍了一种基于会话推荐(ICM-SR)的意向引导协同机器。ICM-SR通过利用先前的顺序项和最后一项来编码正在进行的会话,以生成准确的会话表示,然后将其用于生成初始项预测作为意图。然后,我们设计了一个意图引导的邻居检测器来定位正确的邻居会话。最后,通过门控融合层自适应结合当前会话和邻居会话的表示,产生最终的项目推荐。在两个公共基准数据集上进行的实验表明,ICM-SR在召回率和MRR方面比最先进的基线有了显著的提高。
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引用次数: 24
EARS 2020: The 3rd International Workshop on ExplainAble Recommendation and Search EARS 2020:第三届可解释推荐和搜索国际研讨会
Yongfeng Zhang, Xu Chen, Yi Zhang, Min Zhang, C. Shah
Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also interpretability of the models or explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The workshop focuses on the research and application of explainable recommendation, search, and a broader scope of IR tasks. It will gather researchers as well as practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations regarding AI interpretability, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond.
可解释的推荐和搜索试图开发模型或方法,不仅产生高质量的推荐或搜索结果,而且模型或结果的解释对于用户或系统设计者来说是可解释性的,这有助于提高系统的透明度、说服力、可信度和有效性等。这在个性化搜索和推荐场景中更为重要,用户想知道为什么特定的产品、网页、新闻报道或朋友建议会出现在他或她自己的搜索和推荐列表中。研讨会的重点是可解释的推荐、搜索和更广泛的红外任务的研究和应用。它将聚集该领域的研究人员和实践者进行讨论、思想交流和研究推广。它还将引发有关人工智能可解释性的最新法规的深刻辩论,涉及更广泛的社区,包括但不限于人工智能、机器学习、人工智能、数据科学等。
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引用次数: 2
Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation 基于块级搜索推荐优化的混合目标指向解码器训练
Harsh Kohli
Related or ideal follow-up suggestions to a web query in search engines are often optimized based on several different parameters -- relevance to the original query, diversity, click probability etc. One or many rankers may be trained to score each suggestion from a candidate pool based on these factors. These scorers are usually pairwise classification tasks where each training example consists of a user query and a single suggestion from the list of candidates. We propose an architecture that takes all candidate suggestions associated with a given query and outputs a suggestion block. We discuss the benefits of such an architecture over traditional approaches and experiment with further enforcing each individual metric through mixed-objective training.
搜索引擎中与网页查询相关或理想的后续建议通常基于几个不同的参数进行优化——与原始查询的相关性、多样性、点击概率等。可以训练一个或多个排名员,根据这些因素对候选池中的每个建议进行评分。这些评分器通常是成对分类任务,其中每个训练示例由用户查询和候选列表中的单个建议组成。我们提出了一个架构,它接受与给定查询关联的所有候选建议,并输出一个建议块。我们讨论了这种体系结构相对于传统方法的好处,并通过混合目标训练进一步加强每个单独的度量。
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引用次数: 1
Extractive Snippet Generation for Arguments 为参数生成提取代码段
Milad Alshomary, Nick Düsterhus, Henning Wachsmuth
Snippets are used in web search to help users assess the relevance of retrieved results to their query. Recently, specialized search engines have arisen that retrieve pro and con arguments on controversial issues. We argue that standard snippet generation is insufficient to represent the core reasoning of an argument. In this paper, we introduce the task of generating a snippet that represents the main claim and reason of an argument. We propose a query-independent extractive summarization approach to this task that uses a variant of PageRank to assess the importance of sentences based on their context and argumentativeness. In both automatic and manual evaluation, our approach outperforms strong baselines.
在网络搜索中使用片段来帮助用户评估检索结果与他们的查询的相关性。最近,专门的搜索引擎出现了,检索有争议问题的赞成和反对意见。我们认为,标准的代码片段生成不足以表示论点的核心推理。在本文中,我们介绍了生成一个代表论点的主要主张和理由的片段的任务。我们提出了一种独立于查询的提取摘要方法,该方法使用PageRank的变体来评估基于上下文和论证性的句子的重要性。在自动和手动评估中,我们的方法都优于强基线。
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引用次数: 20
Searching the Web for Cross-lingual Parallel Data 搜索跨语言并行数据的网络
Ahmed El-Kishky, Philipp Koehn, Holger Schwenk
While the World Wide Web provides a large amount of text in many languages, cross-lingual parallel data is more difficult to obtain. Despite its scarcity, this parallel cross-lingual data plays a crucial role in a variety of tasks in natural language processing with applications in machine translation, cross-lingual information retrieval, and document classification, as well as learning cross-lingual representations. Here, we describe the end-to-end process of searching the web for parallel cross-lingual texts. We motivate obtaining parallel text as a retrieval problem whereby the goal is to retrieve cross-lingual parallel text from a large, multilingual web-crawled corpus. We introduce techniques for searching for cross-lingual parallel data based on language, content, and other metadata. We motivate and introduce multilingual sentence embeddings as a core tool and demonstrate techniques and models that leverage them for identifying parallel documents and sentences as well as techniques for retrieving and filtering this data. We describe several large-scale datasets curated using these techniques and show how training on sentences extracted from parallel or comparable documents mined from the Web can improve machine translation models and facilitate cross-lingual NLP.
虽然万维网提供了多种语言的大量文本,但跨语言并行数据更难获得。尽管缺乏这种并行的跨语言数据,但它在自然语言处理的各种任务中发挥着至关重要的作用,包括机器翻译、跨语言信息检索和文档分类,以及学习跨语言表示。在这里,我们描述了在网络上搜索平行跨语言文本的端到端过程。我们将获取平行文本作为一个检索问题,其目标是从一个大型的、多语言的网络抓取语料库中检索跨语言的平行文本。我们介绍了基于语言、内容和其他元数据搜索跨语言并行数据的技术。我们鼓励并引入多语言句子嵌入作为核心工具,并演示利用它们来识别并行文档和句子的技术和模型,以及检索和过滤这些数据的技术。我们描述了使用这些技术整理的几个大规模数据集,并展示了如何对从Web挖掘的平行或可比文档中提取的句子进行训练,从而改进机器翻译模型并促进跨语言NLP。
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引用次数: 5
期刊
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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