一周推荐系统论文资讯
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。
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
comment: The definitive Version of Record was published in KDD'23, http://dx.doi.org/10.1145/3580305.3599918
reference: None
TransAct:基于Transformer的Pinterest实时推荐用户行为模型
http://arxiv.org/abs/2306.00248v1
Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems. Traditional methods of sequential recommendation either utilize end-to-end learning on realtime user actions, or learn user representations separately in an offline batch-generated manner. This paper (1) presents Pinterest's ranking architecture for Homefeed, our personalized recommendation product and the largest engagement surface; (2) proposes TransAct, a sequential model that extracts users' short-term preferences from their realtime activities; (3) describes our hybrid approach to ranking, which combines end-to-end sequential modeling via TransAct with batch-generated user embeddings. The hybrid approach allows us to combine the advantages of responsiveness from learning directly on realtime user activity with the cost-effectiveness of batch user representations learned over a longer time period. We describe the results of ablation studies, the challenges we faced during productionization, and the outcome of an online A/B experiment, which validates the effectiveness of our hybrid ranking model. We further demonstrate the effectiveness of TransAct on other surfaces such as contextual recommendations and search. Our model has been deployed to production in Homefeed, Related Pins, Notifications, and Search at Pinterest.
为下一步行动预测编码用户活动的序列模型已经成为构建网络规模个性化推荐系统的流行设计选择。传统的顺序推荐方法要么利用实时用户动作的端到端学习，要么以离线批量生成的方式单独学习用户表示。本文（1）介绍了Pinterest对Homefeed的排名架构，Homefeed是我们的个性化推荐产品，也是最大的参与面；（2） 提出了TransAct，一种从用户的实时活动中提取用户短期偏好的序列模型；（3） 描述了我们的混合排序方法，该方法将通过TransAct进行的端到端顺序建模与批量生成的用户嵌入相结合。混合方法使我们能够将直接对实时用户活动进行学习的响应性优势与在较长时间内学习的批量用户表示的成本效益相结合。我们描述了消融研究的结果，我们在生产过程中面临的挑战，以及在线A/B实验的结果，这验证了我们的混合排名模型的有效性。我们进一步证明了TransAct在其他方面的有效性，如上下文推荐和搜索。我们的模型已在Pinterest的Homefeed、Related Pins、Notifications和Search中部署到生产中。
Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
comment: 12 pages, 10 figures, 5 tables; 29th ACM SIGKDD Conference on Knowledge Discovery & Data (KDD 2023) (to appear) (Please cite our conference version.)
reference: None
准则告诉你的不仅仅是评级：有效多准则推荐的准则偏好感知轻图卷积
http://arxiv.org/abs/2305.18885v2
The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various recommender systems due to GNN's high expressive capability in learning graph representations, it has been still unexplored how to design MC recommender systems with GNNs. In light of this, we make the first attempt towards designing a GNN-aided MC recommender system. Specifically, rather than straightforwardly adopting existing GNN-based recommendation methods, we devise a novel criteria preference-aware light graph convolution CPA-LGC method, which is capable of precisely capturing the criteria preference of users as well as the collaborative signal in complex high-order connectivities. To this end, we first construct an MC expansion graph that transforms user--item MC ratings into an expanded bipartite graph to potentially learn from the collaborative signal in MC ratings. Next, to strengthen the capability of criteria preference awareness, CPA-LGC incorporates newly characterized embeddings, including user-specific criteria-preference embeddings and item-specific criterion embeddings, into our graph convolution model. Through comprehensive evaluations using four real-world datasets, we demonstrate (a) the superiority over benchmark MC recommendation methods and benchmark recommendation methods using GNNs with tremendous gains, (b) the effectiveness of core components in CPA-LGC, and (c) the computational efficiency.
多准则（MC）推荐系统在广泛的电子商务领域中利用MC评级信息，如今无处不在。令人惊讶的是，尽管图神经网络（GNN）由于其在学习图表示方面的高表达能力而被广泛应用于开发各种推荐系统，但如何利用GNN设计MC推荐系统仍有待探索。有鉴于此，我们首次尝试设计一个GNN辅助的MC推荐系统。具体而言，我们没有直接采用现有的基于GNN的推荐方法，而是设计了一种新的标准偏好感知的光图卷积CPA-LGC方法，该方法能够准确捕捉用户的标准偏好以及复杂高阶连通性中的协作信号。为此，我们首先构建了一个MC扩展图，将用户-项目MC评级转换为扩展的二分图，以潜在地从MC评级中的协作信号中学习。接下来，为了增强标准偏好感知的能力，CPA-LGC将新表征的嵌入，包括用户特定的标准偏好嵌入和项目特定的标准嵌入，纳入我们的图卷积模型中。通过使用四个真实世界数据集进行综合评估，我们展示了（a）与基准MC推荐方法和使用GNN的基准推荐方法相比的优势，并获得了巨大的收益，（b）CPA-LGC中核心组件的有效性，以及（c）计算效率。
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
comment: KDD 2023
reference: None
推荐系统中基于图的模型不可知数据子采样
http://arxiv.org/abs/2305.16391v1
Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. Specifically, we study the topology of the user-item graph to estimate the importance of each user-item interaction (an edge in the user-item graph) via graph conductance, followed by a propagation step on the network to smooth out the estimated importance value. Since our proposed method is model-agnostic, we can marry the merits of both model-agnostic and model-based subsampling methods. Empirically, we show that combing the two consistently improves over any single method on the used datasets. Experimental results on KuaiRec and MIND datasets demonstrate that our proposed methods achieve superior results compared to baseline approaches.
数据子采样被广泛用于加速大规模推荐系统的训练。大多数子采样方法都是基于模型的，并且通常需要预先训练的试点模型来通过例如样本硬度来测量数据的重要性。然而，当导频模型被错误指定时，基于模型的子采样方法会恶化。由于模型错误指定在真实的推荐系统中是持久的，因此我们提出了模型不可知的数据子采样方法，只通过探索图表示的输入数据结构。具体来说，我们研究了用户-项目图的拓扑结构，通过图电导来估计每个用户-项目交互的重要性（用户-项目图中的一条边），然后在网络上进行传播步骤，以平滑估计的重要性值。由于我们提出的方法是模型不可知的，我们可以结合模型不可知和基于模型的子采样方法的优点。根据经验，我们表明，在使用的数据集上，将两者结合起来比任何单一方法都能持续改进。在KuaiRec和MIND数据集上的实验结果表明，与基线方法相比，我们提出的方法取得了更好的结果。
Text Is All You Need: Learning Language Representations for Sequential Recommendation
comment: accepted to KDD 2023
reference: None
文本就是你所需要的：学习顺序推荐的语言表达
http://arxiv.org/abs/2305.13731v2
Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge to new datasets. In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets. To this end, we present a novel framework, named Recformer, which effectively learns language representations for sequential recommendation. Specifically, we propose to formulate an item as a "sentence" (word sequence) by flattening item key-value attributes described by text so that an item sequence for a user becomes a sequence of sentences. For recommendation, Recformer is trained to understand the "sentence" sequence and retrieve the next "sentence". To encode item sequences, we design a bi-directional Transformer similar to the model Longformer but with different embedding layers for sequential recommendation. For effective representation learning, we propose novel pretraining and finetuning methods which combine language understanding and recommendation tasks. Therefore, Recformer can effectively recommend the next item based on language representations. Extensive experiments conducted on six datasets demonstrate the effectiveness of Recformer for sequential recommendation, especially in low-resource and cold-start settings.
顺序推荐旨在根据历史交互对动态用户行为进行建模。现有的方法依赖于显式项目ID或用于序列建模的通用文本特征来理解用户偏好。尽管这些方法很有前景，但仍难以对冷启动项目进行建模或将知识转移到新的数据集。在本文中，我们建议将用户偏好和项目特征建模为可以推广到新项目和数据集的语言表示。为此，我们提出了一个新的框架，名为Recformer，它可以有效地学习顺序推荐的语言表示。具体来说，我们建议通过将文本描述的项目键值属性扁平化，将项目公式化为“句子”（单词序列），从而使用户的项目序列变成句子序列。作为推荐，Recformer被训练来理解“句子”序列并检索下一个“句子”。为了对项目序列进行编码，我们设计了一个类似于Longformer模型的双向Transformer，但具有不同的嵌入层，用于顺序推荐。为了有效的表示学习，我们提出了新的预训练和微调方法，将语言理解和推荐任务相结合。因此，Recformer可以根据语言表示有效地推荐下一个项目。在六个数据集上进行的大量实验证明了Recformer在顺序推荐方面的有效性，尤其是在低资源和冷启动环境中。
Curse of "Low" Dimensionality in Recommender Systems
comment: Accepted by SIGIR'23
reference: None
推荐系统中“低”维度的诅咒
http://arxiv.org/abs/2305.13597v1
Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness. We argue that many of the prevalent problems in recommender systems are partly due to low-dimensionality of user and item embeddings, particularly when dot-product models, such as matrix factorization, are used. In this study, we showcase empirical evidence suggesting the necessity of sufficient dimensionality for user/item embeddings to achieve diverse, fair, and robust recommendation. We then present theoretical analyses of the expressive power of dot-product models. Our theoretical results demonstrate that the number of possible rankings expressible under dot-product models is exponentially bounded by the dimension of item factors. We empirically found that the low-dimensionality contributes to a popularity bias, widening the gap between the rank positions of popular and long-tail items; we also give a theoretical justification for this phenomenon.
除了准确性之外，推荐系统的质量还有很多方面，如多样性、公平性和稳健性。我们认为，推荐系统中的许多普遍问题部分是由于用户和项目嵌入的低维，特别是当使用点积模型时，如矩阵分解。在这项研究中，我们展示了经验证据，表明用户/项目嵌入需要足够的维度，以实现多样化、公平和稳健的推荐。然后，我们对点积模型的表现力进行了理论分析。我们的理论结果表明，在点积模型下可以表达的可能排名的数量是由项目因素的维度指数限制的。我们实证发现，低维度导致了流行偏好，扩大了流行项目和长尾项目的排名位置之间的差距；我们也为这一现象提供了理论依据。
Multi-behavior Self-supervised Learning for Recommendation
comment: SIGIR 2023
reference: None
推荐的多行为自监督学习
http://arxiv.org/abs/2305.18238v1
Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understand and leverage multi-behavior data from users. Despite recent efforts towards making use of heterogeneous data, multi-behavior recommendation still faces great challenges. Firstly, sparse target signals and noisy auxiliary interactions remain an issue. Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task. Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. Specifically, we devise a behavior-aware graph neural network incorporating the self-attention mechanism to capture behavior multiplicity and dependencies. To increase the robustness to data sparsity under the target behavior and noisy interactions from auxiliary behaviors, we propose a novel self-supervised learning paradigm to conduct node self-discrimination at both inter-behavior and intra-behavior levels. In addition, we develop a customized optimization strategy through hybrid manipulation on gradients to adaptively balance the self-supervised learning task and the main supervised recommendation task. Extensive experiments on five real-world datasets demonstrate the consistent improvements obtained by MBSSL over ten state-of-the art (SOTA) baselines. We release our model implementation at: https://github.com/Scofield666/MBSSL.git.
现代推荐系统通常处理各种用户交互，例如点击、转发、购买等，这需要底层推荐引擎充分理解和利用用户的多行为数据。尽管最近在利用异构数据方面做出了努力，但多行为推荐仍然面临着巨大的挑战。首先，稀疏的目标信号和有噪声的辅助交互仍然是一个问题。其次，现有的利用自监督学习（SSL）来解决数据稀疏性的方法忽略了SSL任务和目标任务之间严重的优化不平衡。因此，我们提出了一种多行为自监督学习（MBSL）框架和自适应优化方法。具体来说，我们设计了一个行为感知图神经网络，结合了自注意机制来捕捉行为的多样性和依赖性。为了提高在目标行为和辅助行为的噪声交互下对数据稀疏性的鲁棒性，我们提出了一种新的自监督学习范式，在行为间和行为内两个层面进行节点自判别。此外，我们通过梯度上的混合操作开发了一种定制的优化策略，以自适应地平衡自监督学习任务和主监督推荐任务。在五个真实世界数据集上进行的大量实验表明，MBSL在十个最先进（SOTA）基线上获得了一致的改进。我们在以下位置发布我们的模型实现：https://github.com/Scofield666/MBSSL.git.