哈喽大家好，这里是小夏机器人的一周推荐系统论文资讯，推送目前最新的顶会论文。由于智力尚浅，中文内容是机翻，因此可能存在例如专业名词翻译不准确的情况，大家参考即可。
When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
comment: Accecpted by SIGIR 2023
reference: None
当搜索满足推荐时：学习推荐的纠缠搜索表示
Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been any effective means of incorporating user behavior data from both S&R services. Most existing approaches either simply treat S&R behaviors separately, or jointly optimize them by aggregating data from both services, ignoring the fact that user intents in S&R can be distinctively different. In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors. Specifically, SESRec first aligns query and item embeddings based on users' query-item interactions for the computations of their similarities. Two transformer encoders are used to learn the contextual representations of S&R behaviors independently. Then a contrastive learning task is designed to supervise the disentanglement of similar and dissimilar representations from behavior sequences of S&R. Finally, we extract user interests by the attention mechanism from three perspectives, i.e., the contextual representations, the two separated behaviors containing similar and dissimilar interests. Extensive experiments on both industrial and public datasets demonstrate that SESRec consistently outperforms state-of-the-art models. Empirical studies further validate that SESRec successfully disentangle similar and dissimilar user interests from their S&R behaviors.
现代在线服务提供商，如在线购物平台，通常同时提供搜索和推荐（S&R）服务，以满足不同的用户需求。很少有任何有效的方法来合并来自S&R服务的用户行为数据。大多数现有的方法要么简单地单独处理S&R行为，要么通过聚合来自两种服务的数据来联合优化它们，忽略了用户在S&R中的意图可能明显不同这一事实。在我们的论文中，我们提出了一个用于顺序推荐的搜索增强框架（SESRec），该框架通过解开S&R行为中相似和不相似的表示来利用用户的搜索兴趣进行推荐。具体来说，SESRec首先基于用户的查询-项目交互来对齐查询和项目嵌入，以计算它们的相似性。两个转换器编码器用于独立学习S&R行为的上下文表示。然后设计了一个对比学习任务来监督从S&R的行为序列中解开相似和不相似的表征。最后，我们通过注意力机制从三个角度提取用户兴趣，即上下文表示，这两种分离的行为包含相似和不同的兴趣。在工业和公共数据集上进行的大量实验表明，SESRec始终优于最先进的模型。实证研究进一步验证了SESRec成功地将相似和不同的用户兴趣与他们的S&R行为区分开来。
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning
comment: SIGIR 2023, Resource Track
reference: None
FedAds:垂直联合学习的隐私保护CVR估计基准
Conversion rate (CVR) estimation aims to predict the probability of conversion event after a user has clicked an ad. Typically, online publisher has user browsing interests and click feedbacks, while demand-side advertising platform collects users' post-click behaviors such as dwell time and conversion decisions. To estimate CVR accurately and protect data privacy better, vertical federated learning (vFL) is a natural solution to combine two sides' advantages for training models, without exchanging raw data. Both CVR estimation and applied vFL algorithms have attracted increasing research attentions. However, standardized and systematical evaluations are missing: due to the lack of standardized datasets, existing studies adopt public datasets to simulate a vFL setting via hand-crafted feature partition, which brings challenges to fair comparison. We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms. It contains a large-scale real world dataset collected from Alibaba's advertising platform, as well as systematical evaluations for both effectiveness and privacy aspects of various vFL algorithms. Besides, we also explore to incorporate unaligned data in vFL to improve effectiveness, and develop perturbation operations to protect privacy well. We hope that future research work in vFL and CVR estimation benefits from the FedAds benchmark.
转化率（CVR）估计旨在预测用户点击广告后发生转化事件的概率。通常，在线发布者有用户的浏览兴趣和点击反馈，而需求侧广告平台收集用户的点击后行为，如停留时间和转化决策。为了准确估计CVR并更好地保护数据隐私，垂直联合学习（vFL）是一种在不交换原始数据的情况下结合双方优势训练模型的自然解决方案。CVR估计和应用的vFL算法都吸引了越来越多的研究关注。然而，缺乏标准化和系统化的评估：由于缺乏标准化的数据集，现有的研究采用公共数据集通过手工制作的特征划分来模拟vFL设置，这给公平比较带来了挑战。我们引入了FedAds，这是第一个使用vFL进行CVR估计的基准，以促进对vFL算法的标准化和系统化评估。它包含了从阿里巴巴广告平台收集的大规模真实世界数据集，以及对各种vFL算法的有效性和隐私方面的系统评估。此外，我们还探索在vFL中加入未对齐的数据以提高有效性，并开发扰动操作以很好地保护隐私。我们希望未来在vFL和CVR估计方面的研究工作能够受益于FedAds基准。
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
comment: 10 Pages, 6 figures, WWW'2023
reference: None
用于基于会话的新项目推荐的双意图增强图神经网络
Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as '\textbf{G}NN \textbf{S}ession-based \textbf{N}ew \textbf{I}tem \textbf{R}ecommendation (GSNIR)'. To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. The code is available at Github: https://github.com/Ee1s/NirGNN
推荐系统对各种领域至关重要，例如电子商务、电子学习和流媒体。目前，用于基于会话的推荐的图神经网络（GNN）通常只能推荐用户历史会话中存在的项目。因此，这些GNN很难推荐用户从未互动过的项目（新项目），这导致了信息茧现象。因此，有必要向用户推荐新项目。由于新项目和用户之间没有交互，因此在为基于GNN会话的推荐系统构建会话图时，我们不能包含新项目。因此，当使用基于GNN的方法时，向用户推荐新项目是具有挑战性的。我们将这一挑战视为“基于会话的\textbf｛G｝NN\textbf{S｝ew\textbf｛I｝tem\textbf｛R｝建议（GSNIR）”。为了解决这个问题，我们提出了一种双重意图增强的图神经网络。由于新项目不与历史会话绑定，用户的意图很难预测。我们设计了一个双意图网络，分别从注意力机制和历史数据的分布中学习用户意图，可以模拟用户在与新项目交互时的决策过程。为了解决GNN无法学习新项目的问题，受零样本学习（ZSL）的启发，我们利用GNN空间中新项目的属性来推断新项目的表示。通过输出包含相应项目的推荐分数的新项目概率，将分数较高的新项目推荐给用户。在两个具有代表性的真实世界数据集上的实验表明了我们提出的方法的优越性。来自真实世界的案例研究验证了双重意图模块和新项目推理模块带来的可解释性优势。该代码可在Github上获得：https://github.com/Ee1s/NirGNN
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
comment: Published as a SIGIR'23 short paper
reference: None
协作过滤中暴露于交互中的流行度消除
Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations exposure of each item is equal or proportional, using inverse propensity weighting, causal intervention, or adversarial training. However, increasing the exposure of unpopular items may not bring more clicks or interactions, resulting in skewed benefits and failing in achieving real reasonable popularity debiasing. In this paper, we propose a new criterion for popularity debiasing, i.e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion. Under the guidance of the criterion, we then propose a debiasing framework with IPL regularization term which is theoretically shown to achieve a win-win situation of both popularity debiasing and recommendation performance. Experiments conducted on four public datasets demonstrate that when equipping two representative collaborative filtering models with our framework, the popularity bias is effectively alleviated while maintaining the recommendation performance.
推荐系统经常受到流行性偏见的影响，流行的项目被过度推荐，同时牺牲了不受欢迎的项目。现有的研究通常侧重于确保每个项目的推荐暴露数量相等或成比例，使用反向倾向加权、因果干预或对抗性训练。然而，增加不受欢迎的项目的曝光率可能不会带来更多的点击或互动，从而导致收益扭曲，无法实现真正合理的流行度去偏倚。在本文中，我们提出了一个新的流行度去偏倚标准，即在一个无偏的推荐系统中，流行和不流行的项目都应该接受与喜欢它的用户数量成比例的交互，即IPL标准。在该准则的指导下，我们提出了一个带有IPL正则化项的去偏框架，理论上表明该框架可以实现流行度去偏和推荐性能的双赢。在四个公共数据集上进行的实验表明，当为两个具有代表性的协同过滤模型配备我们的框架时，在保持推荐性能的同时，有效地缓解了流行度偏差。
Graph Masked Autoencoder for Sequential Recommendation
comment: This paper has been published as a full paper at SIGIR 2023
reference: None
用于序列推荐的图掩码自动编码器
While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labels, Contrastive Learning (CL) has attracted much attention in recent methods to perform data augmentation through embedding contrasting for self-supervision. However, due to the hand-crafted property of their contrastive view generation strategies, existing CL-enhanced models i) can hardly yield consistent performance on diverse sequential recommendation tasks; ii) may not be immune to user behavior data noise. In light of this, we propose a simple yet effective Graph Masked AutoEncoder-enhanced sequential Recommender system (MAERec) that adaptively and dynamically distills global item transitional information for self-supervised augmentation. It naturally avoids the above issue of heavy reliance on constructing high-quality embedding contrastive views. Instead, an adaptive data reconstruction paradigm is designed to be integrated with the long-range item dependency modeling, for informative augmentation in sequential recommendation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity. Our implemented model code is available at https://github.com/HKUDS/MAERec.
虽然一些强大的神经网络架构（例如，Transformer、Graph neural Networks）在使用高阶项目依赖性建模的顺序推荐中实现了改进的性能，但它们在标签稀缺场景中可能表现能力较差。为了解决标签不足的问题，对比学习（CL）在最近通过嵌入对比进行自我监督来进行数据增强的方法中备受关注。然而，由于其对比视图生成策略的手工特性，现有的CL增强模型i）很难在不同的顺序推荐任务上产生一致的性能；ii）可能不能免受用户行为数据噪声的影响。有鉴于此，我们提出了一种简单而有效的图掩码自动编码器增强序列推荐系统（MAERec），该系统自适应地动态提取全局项目过渡信息，用于自监督增强。它自然避免了上述严重依赖于构建高质量嵌入对比视图的问题。相反，自适应数据重建范式被设计为与长期项目依赖性建模相集成，以在顺序推荐中提供信息。大量实验表明，我们的方法显著优于最先进的基线模型，并且可以学习针对数据噪声和稀疏性的更准确的表示。我们实现的模型代码可在https://github.com/HKUDS/MAERec.
Attacking Pre-trained Recommendation
comment: Accepted by SIGIR 2023
reference: None
攻击性预训练建议
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks. Despite these advancements, the vulnerabilities of classical recommender systems also exist in pre-trained recommendation in a new form, while the security of pre-trained recommendation model is still unexplored, which may threaten its widely practical applications. In this study, we propose a novel framework for backdoor attacking in pre-trained recommendation. We demonstrate the provider of the pre-trained model can easily insert a backdoor in pre-training, thereby increasing the exposure rates of target items to target user groups. Specifically, we design two novel and effective backdoor attacks: basic replacement and prompt-enhanced, under various recommendation pre-training usage scenarios. Experimental results on real-world datasets show that our proposed attack strategies significantly improve the exposure rates of target items to target users by hundreds of times in comparison to the clean model.
最近，一系列先驱研究表明了预训练模型在顺序推荐中的效力，为不同的下游推荐任务构建全知全能的统一预训练推荐模型开辟了道路。尽管取得了这些进步，但经典推荐系统的漏洞也以一种新的形式存在于预训练推荐中，而预训练推荐模型的安全性仍有待探索，这可能威胁到其广泛的实际应用。在这项研究中，我们提出了一种新的框架，用于在预先训练的推荐中进行后门攻击。我们证明了预训练模型的提供者可以很容易地在预训练中插入后门，从而提高目标项目对目标用户群体的暴露率。具体来说，我们设计了两种新颖有效的后门攻击：基本替换和即时增强，在各种推荐预训练使用场景下。在真实世界数据集上的实验结果表明，与干净模型相比，我们提出的攻击策略显著提高了目标项目对目标用户的暴露率数百倍。
NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking
comment: 11 pages, 5 figures. 17TH International AAAI Conference on Web and Social Media; Mediate 2023: News Media and Computational Journalism Workshop
reference: None
NewsQuote:一个基于事实核查中专家推荐的报价提取和归因的数据集
To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic. To achieve the aim, we describe the construction of a novel NewsQuote dataset consisting of 24,031 quote-speaker pairs that appeared on a COVID-19 news corpus. We demonstrate an automatic pipeline for speaker and quote extraction via a BERT-based Question Answering model. Then, we formulate expert recommendations as document retrieval task by retrieving relevant quotes first as an intermediate step for expert identification, and expert retrieval by directly retrieving sources based on the probability of a query conditional on a candidate expert. Experimental results on NewsQuote show that document retrieval is more effective in identifying relevant experts for a given news topic compared to expert retrieval
为了提高在新闻文章中找到可信证据的能力，我们提出了一项新的专家推荐任务，旨在确定特定新闻主题上值得信赖的专家。为了实现这一目标，我们描述了一个新的NewsQuote数据集的构建，该数据集由24031对出现在新冠肺炎新闻语料库中的引用语对组成。我们展示了一种通过基于BERT的问答模型进行说话人和引语提取的自动流水线。然后，我们通过首先检索相关引文作为专家识别的中间步骤，将专家推荐公式化为文档检索任务，并通过基于以候选专家为条件的查询的概率直接检索来源来进行专家检索。NewsQuote的实验结果表明，与专家检索相比，文档检索在识别特定新闻主题的相关专家方面更有效