社交媒体营销在向广泛的受众群体推广品牌和产品价值方面起着至关重要的作用。为了提高其广告收入,诸如Facebook广告之类的全球媒体购买平台不断减少品牌有机帖子的覆盖范围,推动品牌在付费媒体广告上花费更多。为了有效地运行有机和付费社交媒体营销,有必要了解受众,调整内容以适合其兴趣和在线行为,这是不可能大规模手动进行的。同时,各种人格类型分类方案(例如Myers-Briggs人格类型指标)使得通过以统一和结构化的方式对受众行为进行分类,可以在更广泛的范围内揭示人格特质和用户内容偏好之间的依赖性。研究界尚待深入研究这个问题,而到目前为止,尚未广泛使用和全面评估,而不同人格特征对内容建议准确性的影响水平尚未得到广泛的利用和全面评估。具体而言,在这项工作中,我们通过应用一种新型人格驱动的多视图内容推荐系统,研究人格特征对内容推荐模型的影响,称为人格内容营销推荐引擎或Persic。我们的实验结果和现实世界案例研究不仅表明Persic执行有效的人格驱动的多视图内容建议,而且还允许采用可行的数字广告策略建议,当部署时能够提高数字广告效率超过420 %与原始的人类指导方法相比。
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受到计算机愿景和语言理解的深度学习的巨大成功的影响,建议的研究已经转移到发明基于神经网络的新推荐模型。近年来,我们在开发神经推荐模型方面目睹了显着进展,这概括和超越了传统的推荐模型,由于神经网络的强烈代表性。在本调查论文中,我们从建议建模与准确性目标的角度进行了系统审查,旨在总结该领域,促进研究人员和从业者在推荐系统上工作的研究人员和从业者。具体而具体基于推荐建模期间的数据使用,我们将工作划分为协作过滤和信息丰富的建议:1)协作滤波,其利用用户项目交互数据的关键来源; 2)内容丰富的建议,其另外利用与用户和项目相关的侧面信息,如用户配置文件和项目知识图; 3)时间/顺序推荐,其考虑与交互相关的上下文信息,例如时间,位置和过去的交互。在为每种类型审查代表性工作后,我们终于讨论了这一领域的一些有希望的方向。
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随着人格计算的出现作为与人工智能和人格心理有关的新研究领域,我们目睹了一个前所未有的人格意识推荐系统的扩散。与传统推荐系统不同,这些新系统解决了传统问题,如冷启动和数据稀疏问题。该调查旨在研究和系统地分类人格意识推荐系统。据我们所知,这项调查是第一个重点关注人格意识推荐系统。通过比较其个性建模方法以及其推荐技术,我们探索了人格感知推荐系统的不同设计选择。此外,我们介绍了常用的数据集,并指出了人格感知推荐系统的一些挑战。
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In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -collaborative filtering -on the basis of implicit feedback.Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural networkbased Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.
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在这个大数据时代,当前一代很难从在线平台中包含的大量数据中找到正确的数据。在这种情况下,需要一个信息过滤系统,可以帮助他们找到所需的信息。近年来,出现了一个称为推荐系统的研究领域。推荐人变得重要,因为他们拥有许多现实生活应用。本文回顾了推荐系统在电子商务,电子商务,电子资源,电子政务,电子学习和电子生活中的不同技术和发展。通过分析有关该主题的最新工作,我们将能够详细概述当前的发展,并确定建议系统中的现有困难。最终结果为从业者和研究人员提供了对建议系统及其应用的必要指导和见解。
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推荐系统是机器学习系统的子类,它们采用复杂的信息过滤策略来减少搜索时间,并向任何特定用户建议最相关的项目。混合建议系统以不同的方式结合了多种建议策略,以从其互补的优势中受益。一些混合推荐系统已经结合了协作过滤和基于内容的方法来构建更强大的系统。在本文中,我们提出了一个混合推荐系统,该系统将基于最小二乘(ALS)的交替正方(ALS)的协作过滤与深度学习结合在一起,以增强建议性能,并克服与协作过滤方法相关的限制,尤其是关于其冷启动问题。本质上,我们使用ALS(协作过滤)的输出来影响深度神经网络(DNN)的建议,该建议结合了大数据处理框架中的特征,上下文,结构和顺序信息。我们已经进行了几项实验,以测试拟议混合体架构向潜在客户推荐智能手机的功效,并将其性能与其他开源推荐人进行比较。结果表明,所提出的系统的表现优于几个现有的混合推荐系统。
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由于出版物的数量越来越多,找到与人的利益相关的在线研究论文非常具有挑战性。因此,个性化的研究论文建议已成为一个重要且及时的研究主题。协作过滤是一种成功的推荐方法,它利用用户给出的项目的评分作为学习的信息来源,以提出准确的建议。但是,由于每年的出版物数量大量增长,评级通常非常稀少。因此,人们对考虑评级和内容信息的混合方法有了更多的关注。然而,基于文本嵌入的大多数混合推荐方法都使用了词袋技术,它们忽略了单词顺序和语义含义。在本文中,我们提出了一种混合方法,该方法基于用户分配的社会标签来利用研究论文的深层语义表示。实验评估是对Citeulike进行的,Citeulike是一个真实且公开可用的数据集。获得的发现表明,即使评级数据非常稀疏,提出的模型也可以有效推荐研究论文。
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传统的推荐系统面临两个长期存在的障碍,即数据稀疏性和冷启动问题,这些问题促进了跨域建议(CDR)的出现和发展。 CDR的核心思想是利用从其他领域收集的信息来减轻一个域中的两个问题。在过去的十年中,许多努力进行了跨域建议。最近,随着深度学习和神经网络的发展,出现了许多方法。但是,关于CDR的系统调查数量有限,尤其是关于最新提出的方法以及他们解决的建议方案和建议任务。在本调查文件中,我们首先提出了跨域建议的两级分类法,该分类法对不同的建议方案和建议任务进行了分类。然后,我们以结构化的方式介绍并总结了不同建议方案下的现有跨域推荐方法。我们还组织了常用的数据集。我们通过提供有关该领域的几个潜在研究方向来结束这项调查。
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本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片(Sota)。它填补了空白,因为现有的调查文章在其范围内或被约会。我们包括两个重要方面,目前正在挖掘和建模社交媒体的重要性:动态和网络。社会动态对于了解影响影响或疾病的传播,友谊的形成,友谊的形成等,另一方面,可以捕获各种复杂关系,提供额外的洞察力和识别否则将不会被注意的重要模式。
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推荐兴趣点是一个困难的问题,需要从基于位置的社交媒体平台中提取精确的位置信息。对于这种位置感知的推荐系统而言,另一个具有挑战性和关键的问题是根据用户的历史行为对用户的偏好进行建模。我们建议使用Transformers的双向编码器表示的位置感知建议系统,以便为用户提供基于位置的建议。提出的模型包含位置数据和用户偏好。与在序列中预测每个位置的下一项(位置)相比,我们的模型可以为用户提供更相关的结果。基准数据集上的广泛实验表明,我们的模型始终优于各种最新的顺序模型。
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近年来,多媒体推荐的兴趣日益增长,旨在预测用户是否会与具有多模式内容的项目进行交互。以前的研究侧重于建模用户项目与包含作为侧面信息的多模式特征的交互。但是,该方案并不适用于多媒体推荐。首先,只有通过高阶项 - 用户项共同发生隐含地建模协作项目 - 项目关系。我们认为这些多模式内容的潜在语义项 - 项目结构可以有利于学习更好的项目表示,并协助推荐模型全面发现候选项目。其次,以前的研究忽视了细粒度的多峰融合。虽然访问多种方式可能允许我们捕获丰富的信息,但我们认为以前的工作中的线性组合或连接的简单粗粒融合不足以完全理解内容信息和项目关系。在此结束时,我们提出了一个潜在的结构采用对比模型融合方法(微型简洁性)。具体而言,我们设计了一种新型的模态感知结构学习模块,它为每个模态学习项目项目关系。基于学习的模态感知潜在项目关系,我们执行明确地将物品关联的图形卷评进行了模当感知的项目表示。然后,我们设计一种新颖的对比方法来保险熔断多模峰特征。这些丰富的项目表示可以插入现有的协作过滤方法,以便更准确的建议。关于现实世界数据集的广泛实验证明了我们在最先进的基线上的方法的优越性。
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In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.
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Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.
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社交媒体网络已成为人们生活的重要方面,它是其思想,观点和情感的平台。因此,自动化情绪分析(SA)对于以其他信息来源无法识别人们的感受至关重要。对这些感觉的分析揭示了各种应用,包括品牌评估,YouTube电影评论和医疗保健应用。随着社交媒体的不断发展,人们以不同形式发布大量信息,包括文本,照片,音频和视频。因此,传统的SA算法已变得有限,因为它们不考虑其他方式的表现力。通过包括来自各种物质来源的此类特征,这些多模式数据流提供了新的机会,以优化基于文本的SA之外的预期结果。我们的研究重点是多模式SA的最前沿领域,该领域研究了社交媒体网络上发布的视觉和文本数据。许多人更有可能利用这些信息在这些平台上表达自己。为了作为这个快速增长的领域的学者资源,我们介绍了文本和视觉SA的全面概述,包括数据预处理,功能提取技术,情感基准数据集以及适合每个字段的多重分类方法的疗效。我们还简要介绍了最常用的数据融合策略,并提供了有关Visual Textual SA的现有研究的摘要。最后,我们重点介绍了最重大的挑战,并调查了一些重要的情感应用程序。
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Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform stateof-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset.
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社交媒体在时装行业中的作用在较少年的情况下一直在盛开。在这项工作中,我们调查了社交媒体平台中时尚相关员额的情绪分析。这项任务有两个主要挑战。首先,必须共同考虑不同方式的信息以进行最终预测。在第二个地方,应考虑一些独特的时尚相关的属性。虽然大多数现有的作品侧重于传统的多模式情绪分析,但它们始终未能利用此任务中的时尚相关的属性。我们提出了一种新颖的框架,共同利用图像视觉,文本,文本以及时尚属性模态来确定情绪类别。我们的模型的一个特征是它提取了时尚属性并将它们与图像视觉信息集成了有效表示。此外,它通过相互关注机制利用时尚属性和邮政文本之间的相互关系。由于没有适合此任务的现有数据集,因此我们准备了超过12K时尚相关的社交媒体帖子的大规模情感分析数据集。进行广泛的实验以证明我们模型的有效性。
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随着深度学习技术扩展到现实世界推荐任务,已经开发出许多深度神经网络的协作滤波(CF)模型基于各种神经结构,例如多层的神经架构将用户项目交互项目投影到潜伏特征空间中Perceptron,自动编码器和图形神经网络。然而,大多数现有的协作过滤系统不充分设计用于处理缺失的数据。特别是,为了在训练阶段注入负信号,这些解决方案很大程度上依赖于未观察到的用户项交互,并且简单地将它们视为负实例,这带来了推荐性能下降。为了解决问题,我们开发了一个协作反射增强的AutoEncoder网络(Cranet),它能够探索从观察到和未观察的用户项交互的可转移知识。 Cranet的网络架构由具有反射接收器网络的集成结构和信息融合自动统计器模块形成,其推荐框架具有在互动和非互动项目上编码隐式用户的成对偏好的能力。另外,基于参数正规化的捆绑重量方案旨在对两级颅骨模型进行鲁棒联合训练。我们终于在对应于两个推荐任务的四个不同基准数据集上进行了实验验证了Cranet,以表明,与各种最先进的推荐技术相比,脱叠用户项交互的负信号提高了性能。我们的源代码可在https://github.com/akaxlh/cranet上获得。
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With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving model design has been rapid in research, we argue that many advances fail to translate into practice because of two limiting assumptions. First, most approaches focus on a transductive learning setting which cannot handle unseen users or items and second, many existing methods are developed for static settings that cannot incorporate new data as it becomes available. We argue that these are largely impractical assumptions on real-world platforms where new user interactions happen in real time. In this survey paper, we formalize both concepts and contextualize recommender systems work from the last six years. We then discuss why and how future work should move towards inductive learning and incremental updates for recommendation model design and evaluation. In addition, we present best practices and fundamental open challenges for future research.
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Social recommender systems (SocialRS) simultaneously leverage user-to-item interactions as well as user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of Graph Neural Networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder, 2 groups of decoder, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize the benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions.
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Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 150 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to specific research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent, and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.
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