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.

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.

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.

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。

在本文中，我们提出了一种方法，用于预测社交媒体对等体之间的信任链接，其中一个是在多识别信任建模的人工智能面积。特别是，我们提出了一种数据驱动的多面信任信任建模，该信任建模包括许多不同的特征以进行全面分析。我们专注于展示类似用户的聚类如何实现关键新功能：支持更个性化的，从而为用户提供更准确的预测。在信任感知项目推荐任务中说明，我们在大yelp数据集的上下文中评估所提出的框架。然后，我们讨论如何提高社交媒体的可信关系的检测可以帮助在最近爆发的社交网络环境中支持在线用户的违法行为和谣言的传播。我们的结论是关于一个特别易受资助的用户基础，老年人的反思，以说明关于用户组的推理价值，期望通过通过数据分析获得的洞察力集成已知偏好的一些未来方向。

Such systems are particularly useful for entertainment products such as movies, music, and TV shows. Many customers will view the same movie, and each customer is likely to view numerous different movies. Customers have proven willing to indicate their level of satisfaction with particular movies, so a huge volume of data is available about which movies appeal to which customers. Companies can analyze this data to recommend movies to particular customers. RecommendeR system stRategiesBroadly speaking, recommender systems are based on one of two strategies. The content filtering approach creates a profile for each user or product to characterize its nature. For example, a movie profile could include attributes regarding its genre, the participating actors, its box office popularity, and so forth. User profiles might include demographic information or answers provided on a suitable questionnaire. The profiles allow programs to associate users with matching products. Of course, content-based strategies require gathering external information that might not be available or easy to collect.A known successful realization of content filtering is the Music Genome Project, which is used for the Internet radio service Pandora.com. A trained music analyst scores M odern consumers are inundated with choices. Electronic retailers and content providers offer a huge selection of products, with unprecedented opportunities to meet a variety of special needs and tastes. Matching consumers with the most appropriate products is key to enhancing user satisfaction and loyalty. Therefore, more retailers have become interested in recommender systems, which analyze patterns of user interest in products to provide personalized recommendations that suit a user's taste. Because good personalized recommendations can add another dimension to the user experience, e-commerce leaders like Amazon.com and Netflix have made recommender systems a salient part of their websites.

隐式反馈经常用于开发个性化的推荐服务，因为其无处不在和现实世界中的可访问性。为了有效地利用此类信息，大多数研究都采用成对排名方法对构建的培训三胞胎（用户，正面项目，负项目），并旨在区分每个用户的正面项目和负面项目。但是，这些方法中的大多数都同样对待所有训练三胞胎，这忽略了不同的正或负项目之间的微妙差异。另一方面，即使其他一些作品利用用户行为的辅助信息（例如，停留时间）来捕获这种微妙的差异，但很难获得这样的辅助信息。为了减轻上述问题，我们提出了一个名为Triplet重要性学习（TIL）的新型培训框架，该框架可以自适应地学习训练三胞胎的重要性得分。我们为重要性得分生成的两种策略设计了两种策略，并将整个过程作为双层优化，这不需要任何基于规则的设计。我们将提出的训练程序与基于图形神经网络（GNN）基于图形的推荐模型的几个矩阵分解（MF）集成在一起，证明了我们的框架的兼容性。通过使用与许多最先进方法的三个现实世界数据集进行比较，我们表明我们所提出的方法在top-k推荐方面的召回@k方面优于3-21 \％的最佳现有模型。

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。

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.

大多数现有推荐系统仅基于评级数据，并且他们忽略了可能会增加建议质量的其他信息来源，例如文本评论或用户和项目特征。此外，这些系统的大多数仅适用于小型数据集（数千个观察）并且无法处理大型数据集（具有数百万观察结果）。我们提出了一种推荐人算法，该算法将评级建模技术（即潜在因子模型）与基于文本评论（即潜在Dirichlet分配）的主题建模方法组合，并且我们扩展了算法，使其允许添加额外的用户和项目 - 对系统的特定信息。我们使用具有不同大小的Amazon.com数据集来评估算法的性能，对应于23个产品类别。将建筑模型与四种其他型号进行比较后，我们发现将患有评级的文本评语相结合，导致更好的建议。此外，我们发现为模型添加额外的用户和项目功能会提高其预测精度，这对于中型和大数据集尤其如此。

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。

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.

最近提出的协作度量学习（CML）范式由于其简单性和有效性引起了人们对推荐系统（RS）领域的广泛兴趣。通常，CML的现有文献在很大程度上取决于\ textit {负抽样}策略，以减轻成对计算的耗时负担。但是，在这项工作中，通过进行理论分析，我们发现负抽样会导致对概括误差的偏差估计。具体而言，我们表明，基于抽样的CML将在概括性结合中引入一个偏差项，该术语是由per-use \ textit {total方差}（TV）量化的，在负面采样和地面真相分布引起的分布之间。这表明，即使有足够大的训练数据，优化基于采样的CML损耗函数也不能确保小概括误差。此外，我们表明偏见术语将消失，而无需负面抽样策略。在此激励的情况下，我们提出了一种有效的替代方案，而没有对CML进行负面采样的cml，name \ textit {无抽样协作度量学习}（SFCML），以消除实际意义上的采样偏见。最后，超过七个基准数据集的全面实验表达了所提出的算法的优势。

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。

Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive knearest-neighbor (kNN). Even though these methods are designed for the item prediction task of personalized ranking, none of them is directly optimized for ranking. In this paper we present a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem. We also provide a generic learning algorithm for optimizing models with respect to BPR-Opt. The learning method is based on stochastic gradient descent with bootstrap sampling. We show how to apply our method to two state-of-the-art recommender models: matrix factorization and adaptive kNN. Our experiments indicate that for the task of personalized ranking our optimization method outperforms the standard learning techniques for MF and kNN. The results show the importance of optimizing models for the right criterion.

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。

多臂匪徒（MAB）提供了一种原则性的在线学习方法，以达到探索和剥削之间的平衡。由于表现出色和反馈学习低，没有学习在多种情况下采取行动，因此多臂匪徒在诸如推荐系统等应用程序中引起了广泛的关注。同样，在推荐系统中，协作过滤（CF）可以说是推荐系统中最早，最具影响力的方法。至关重要的是，新用户和不断变化的推荐项目池是推荐系统需要解决的挑战。对于协作过滤，经典方法是训练模型离线，然后执行在线测试，但是这种方法无法再处理用户偏好的动态变化，即所谓的冷启动。那么，如何在没有有效信息的情况下有效地向用户推荐项目？为了解决上述问题，已经提出了一个基于多臂强盗的协作过滤推荐系统，名为BanditMF。 BANDITMF旨在解决多军强盗算法和协作过滤中的两个挑战：（1）如何在有效信息稀缺的条件下解决冷启动问题以进行协作过滤，（2）强大社会关系域中的强盗算法问题是由独立估计与每个用户相关的未知参数并忽略用户之间的相关性引起的。

会话推荐系统提供互动，参与用户的互动方式的承诺，以查找他们喜欢的物品。我们寻求通过三维提高对话建议：1）我们的目标是模仿建议的常见人类互动模式：专家证明他们的建议，寻求者解释为什么他们不喜欢该项目，双方遍历对话框迭代对话框找到合适的物品。 2）我们利用对会话批评的想法来允许用户通过批评主观方面灵活地与自然语言理由进行互动。 3）我们将会话建议适应更广泛的域名，其中不可用的人群地面真理对话框。我们开发了一个新的两部分框架，用于培训会话推荐系统。首先，我们培训推荐制度，共同建议项目，并用主观方面证明其推理。然后，我们微调该模型通过自我监督的机器人播放来合并迭代用户反馈。三个真实数据集的实验表明，与最先进的方法相比，我们的系统可以应用于各种域的不同推荐模型，以实现对话建议的卓越性能。我们还评估了我们对人类用户的模型，显示在我们的框架下培训的系统提供更有用，有用，有用，并且在热情和冷启动设置中提供的知识推荐。

历史互动是推荐模型培训的默认选择，通常表现出高稀疏性，即大多数用户项目对都是未观察到的缺失数据。标准选择是将缺失的数据视为负训练样本，并估计用户项目对之间的相互作用以及观察到的相互作用。通过这种方式，在训练过程中不可避免地会误标记一些潜在的互动，这将损害模型的保真度，阻碍模型回忆起错误标签的项目，尤其是长尾尾。在这项工作中，我们从新的不确定性的新角度研究了标签的问题，该问题描述了缺失数据的固有随机性。随机性促使我们超越了相互作用的可能性，并接受了不确定性建模。为此，我们提出了一个新的不确定性不确定性建议（AUR）框架，该框架由新的不确定性估计器以及正常的推荐模型组成。根据核心不确定性理论，我们得出了一个新的建议目标来学习估计量。由于错误标签的机会反映了一对的潜力，因此AUR根据不确定性提出了建议，该建议被证明是为了改善较不受欢迎的项目的建议性能而不会牺牲整体性能。我们在三个代表性推荐模型上实例化AUR：来自主流模型体系结构的矩阵分解（MF），LightGCN和VAE。两个现实世界数据集的广泛结果验证了AUR W.R.T.的有效性。更好的建议结果，尤其是在长尾项目上。

Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently. The learned attention weights capture sequential dynamics in user behavior and generalize well. Motivated by the special structure of learned parameter space, we question if it is possible to mimic it with an alternative and more lightweight approach. We develop a new tensor factorization-based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture and compares competitively to its neural counterpart.