【推荐系统】推荐系统领域最新研究进展

本文精选了上周（1024-1030）最新发布的17篇推荐系统相关论文。

本次论文集合的方向主要包括可解释性推荐[1]、基于强化学习的推荐算法[2]、深度混合推荐算法[4]、序列化推荐[5,8,15,16]、自监督推荐算法[10]、基于量子计算的因子分解机推荐[11]、鲁棒推荐算法[12,13]、基于隐私保护的推荐算法[14]等。

以下整理了论文标题以及摘要，如感兴趣可移步原文精读。

• 1. COFFEE: Counterfactual Fairness for Personalized Text Generation in  Explainable Recommendation
• 2. Fine-Grained Session Recommendations in E-commerce using Deep  Reinforcement Learning
• 3. Taxonomic Recommendations of Real Estate Properties with Textual  Attribute Information
• 4. Deep Latent Mixture Model for Recommendation
• 5. Disentangling Past-Future Modeling in Sequential Recommendation via Dual  Networks
• 6. Empowering Long-tail Item Recommendation through Cross Decoupling  Network (CDN)
• 7. Goal-Driven Context-Aware Next Service Recommendation for Mashup  Composition
• 8. Sequential Recommendation with Auxiliary Item Relationships via  Multi-Relational Transformer
• 9. Coupling User Preference with External Rewards to Enable Driver-centered  and Resource-aware EV Charging Recommendation
• 10. Self-supervised Graph-based Point-of-interest Recommendation
• 11. Implementation of Trained Factorization Machine Recommendation System on  Quantum Annealer
• 12. Triplet Losses-based Matrix Factorization for Robust Recommendations
• 13. Towards Robust Recommender Systems via Triple Cooperative Defense
• 14. FedGRec: Federated Graph Recommender System with Lazy Update of Latent  Embeddings
• 15. Heterogeneous Information Crossing on Graphs for Session-based  Recommender Systems
• 16. Learning Vector-Quantized Item Representation for Transferable  Sequential Recommenders
• 17. Towards Employing Recommender Systems for Supporting Data and Algorithm  Sharing

1. COFFEE: Counterfactual Fairness for Personalized Text Generation in  Explainable Recommendation

Nan Wang, Shaoliang Nie, Qifan Wang, Yi-Chia Wang, Maziar Sanjabi, Jingzhou Liu, Hamed Firooz, Hongning Wang

​​https://arxiv.org/abs/2210.15500​​

Personalized text generation has broad industrial applications, such as explanation generation for recommendations, conversational systems, etc. Personalized text generators are usually trained on user written text, e.g., reviews collected on e-commerce platforms. However, due to historical, social, or behavioral reasons, there may exist bias that associates certain linguistic quality of user written text with the users' protected attributes such as gender, race, etc. The generators can identify and inherit these correlations and generate texts discriminately w.r.t. the users' protected attributes. Without proper intervention, such bias can adversarially influence the users' trust and reliance on the system. From a broader perspective, bias in auto-generated contents can reinforce the social stereotypes about how online users write through interactions with the users.

2. Fine-Grained Session Recommendations in E-commerce using Deep  Reinforcement Learning

Diddigi Raghu Ram Bharadwaj, Lakshya Kumar, Saif Jawaid, Sreekanth Vempati

​​https://arxiv.org/abs/2210.15451​​

Sustaining users' interest and keeping them engaged in the platform is very important for the success of an e-commerce business. A session encompasses different activities of a user between logging into the platform and logging out or making a purchase. User activities in a session can be classified into two groups: Known Intent and Unknown intent. Known intent activity pertains to the session where the intent of a user to browse/purchase a specific product can be easily captured. Whereas in unknown intent activity, the intent of the user is not known. For example, consider the scenario where a user enters the session to casually browse the products over the platform, similar to the window shopping experience in the offline setting. While recommending similar products is essential in the former, accurately understanding the intent and recommending interesting products is essential in the latter setting in order to retain a user. In this work, we focus primarily on the unknown intent setting where our objective is to recommend a sequence of products to a user in a session to sustain their interest, keep them engaged and possibly drive them towards purchase. We formulate this problem in the framework of the Markov Decision Process (MDP), a popular mathematical framework for sequential decision making and solve it using Deep Reinforcement Learning (DRL) techniques. However, training the next product recommendation is difficult in the RL paradigm due to large variance in browse/purchase behavior of the users. Therefore, we break the problem down into predicting various product attributes, where a pattern/trend can be identified and exploited to build accurate models. We show that the DRL agent provides better performance compared to a greedy strategy.

3. Taxonomic Recommendations of Real Estate Properties with Textual  Attribute Information

Zachary Harrison, Anish Khazane

​​https://arxiv.org/abs/2210.15142​​

In this extended abstract, we present an end to end approach for building a taxonomy of home attribute terms that enables hierarchical recommendations of real estate properties. We cover the methodology for building a real-estate taxonomy, metrics for measuring this structure's quality, and then conclude with a production use-case of making recommendations from search keywords at different levels of topical similarity.

4. Deep Latent Mixture Model for Recommendation

Jun Zhang, Ping Li, Wei Wang

​​https://arxiv.org/abs/2210.15112​​

Recent advances in neural networks have been successfully applied to many tasks in online recommendation applications. We propose a new framework called cone latent mixture model which makes use of hand-crafted state being able to factor distinct dependencies among multiple related documents. Specifically, it uses discriminative optimization techniques in order to generate effective multi-level knowledge bases, and uses online discriminative learning techniques in order to leverage these features. And for this joint model which uses confidence estimates for each topic and is able to learn a discriminatively trained jointly to automatically extracted salient features where discriminative training is only uses features and then is able to accurately trained.

5. Disentangling Past-Future Modeling in Sequential Recommendation via Dual  Networks

Hengyu Zhang, Enming Yuan, Wei Guo, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Xiu Li, Ruiming Tang

​​https://arxiv.org/abs/2210.14577​​

Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors. Unlike the standard autoregressive training strategy, future data (also available during training) has been used to facilitate model training as it provides richer signals about user's current interests and can be used to improve the recommendation quality. However, these methods suffer from a severe training-inference gap, i.e., both past and future contexts are modeled by the same encoder when training, while only historical behaviors are available during inference. This discrepancy leads to potential performance degradation. To alleviate the training-inference gap, we propose a new framework DualRec, which achieves past-future disentanglement and past-future mutual enhancement by a novel dual network. Specifically, a dual network structure is exploited to model the past and future context separately. And a bi-directional knowledge transferring mechanism enhances the knowledge learnt by the dual network. Extensive experiments on four real-world datasets demonstrate the superiority of our approach over baseline methods. Besides, we demonstrate the compatibility of DualRec by instantiating using RNN, Transformer, and filter-MLP as backbones. Further empirical analysis verifies the high utility of modeling future contexts under our DualRec framework.

6. Empowering Long-tail Item Recommendation through Cross Decoupling  Network (CDN)

Yin Zhang, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi

​​https://arxiv.org/abs/2210.14309​​

Recommenders provide personalized content recommendations to users. They often suffer from highly skewed long-tail item distributions, with a small fraction of the items receiving most of the user feedback. This hurts model quality especially for the slices without much supervision. Existing work in both academia and industry mainly focuses on re-balancing strategies (e.g., up-sampling and up-weighting), leveraging content features, and transfer learning. However, there still lacks of a deeper understanding of how the long-tail distribution influences the recommendation performance.

7. Goal-Driven Context-Aware Next Service Recommendation for Mashup  Composition

Xihao Xie, Jia Zhang, Rahul Ramachandran, Tsengdar J. Lee, Seungwon Lee

​​https://arxiv.org/abs/2210.14127​​

As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services. To create a mashup, it gets not only time-consuming but also error-prone for developers to find suitable services from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected to the current step as well as its mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of our approach.

8. Sequential Recommendation with Auxiliary Item Relationships via  Multi-Relational Transformer

Ziwei Fan, Zhiwei Liu, Chen Wang, Peijie Huang, Hao Peng, Philip S. Yu

​​https://arxiv.org/abs/2210.13572​​

Sequential Recommendation (SR) models user dynamics and predicts the next preferred items based on the user history. Existing SR methods model the 'was interacted before' item-item transitions observed in sequences, which can be viewed as an item relationship. However, there are multiple auxiliary item relationships, e.g., items from similar brands and with similar contents in real-world scenarios. Auxiliary item relationships describe item-item affinities in multiple different semantics and alleviate the long-lasting cold start problem in the recommendation. However, it remains a significant challenge to model auxiliary item relationships in SR.

9. Coupling User Preference with External Rewards to Enable Driver-centered  and Resource-aware EV Charging Recommendation

Chengyin Li, Zheng Dong, Nathan Fisher, Dongxiao Zhu

​​https://arxiv.org/abs/2210.12693​​

Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Previous studies focus on centralized strategies to achieve optimized resource allocation, particularly useful for privacy-indifferent taxi fleets and fixed-route public transits. However, private EV driver seeks a more personalized and resource-aware charging recommendation that is tailor-made to accommodate the user preference (when and where to charge) yet sufficiently adaptive to the spatiotemporal mismatch between charging supply and demand. Here we propose a novel Regularized Actor-Critic (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference (historical charging pattern) and the external reward (driving distance and wait time). Experimental results on two real-world datasets demonstrate the unique features and superior performance of our approach to the competing methods.

10. Self-supervised Graph-based Point-of-interest Recommendation

Yang Li, Tong Chen, Peng-Fei Zhang, Zi Huang, Hongzhi Yin

​​https://arxiv.org/abs/2210.12506​​

The exponential growth of Location-based Social Networks (LBSNs) has greatly stimulated the demand for precise location-based recommendation services. Next Point-of-Interest (POI) recommendation, which aims to provide personalised POI suggestions for users based on their visiting histories, has become a prominent component in location-based e-commerce. Recent POI recommenders mainly employ self-attention mechanism or graph neural networks to model complex high-order POI-wise interactions. However, most of them are merely trained on the historical check-in data in a standard supervised learning manner, which fail to fully explore each user's multi-faceted preferences, and suffer from data scarcity and long-tailed POI distribution, resulting in sub-optimal performance. To this end, we propose a Self-s}upervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs to uncover the transitional dependencies among POIs and capture a user's temporal interests. In order to counteract the scarcity and incompleteness of POI check-ins, we propose a novel self-supervised learning paradigm in \ssgrec, where the trajectory representations are contrastively learned from two augmented views on geolocations and temporal transitions. Extensive experiments are conducted on three real-world LBSN datasets, demonstrating the effectiveness of our model against state-of-the-art methods.

11. Implementation of Trained Factorization Machine Recommendation System on  Quantum Annealer

Chen-Yu Liu, Hsin-Yu Wang, Pei-Yen Liao, Ching-Jui Lai, Min-Hsiu Hsieh

​​https://arxiv.org/abs/2210.12953​​

Factorization Machine (FM) is the most commonly used model to build a recommendation system since it can incorporate side information to improve performance. However, producing item suggestions for a given user with a trained FM is time-consuming. It requires a run-time of , where is the number of items in the dataset. To address this problem, we propose a quadratic unconstrained binary optimization (QUBO) scheme to combine with FM and apply quantum annealing (QA) computation. Compared to classical methods, this hybrid algorithm provides a faster than quadratic speedup in finding good user suggestions. We then demonstrate the aforementioned computational advantage on current NISQ hardware by experimenting with a real example on a D-Wave annealer.

12. Triplet Losses-based Matrix Factorization for Robust Recommendations

Flavio Giobergia

​​https://arxiv.org/abs/2210.12098​​

Much like other learning-based models, recommender systems can be affected by biases in the training data. While typical evaluation metrics (e.g. hit rate) are not concerned with them, some categories of final users are heavily affected by these biases. In this work, we propose using multiple triplet losses terms to extract meaningful and robust representations of users and items. We empirically evaluate the soundness of such representations through several "bias-aware" evaluation metrics, as well as in terms of stability to changes in the training set and agreement of the predictions variance w.r.t. that of each user.

13. Towards Robust Recommender Systems via Triple Cooperative Defense

Qingyang Wang, Defu Lian, Chenwang Wu, Enhong Chen

​​https://arxiv.org/abs/2210.13762​​

Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. The wide application of recommender systems makes studying the defense against attack necessary. Among existing defense methods, data-processing-based methods inevitably exclude normal samples, while model-based methods struggle to enjoy both generalization and robustness. Considering the above limitations, we suggest integrating data processing and robust model and propose a general framework, Triple Cooperative Defense (TCD), which cooperates to improve model robustness through the co-training of three models. Specifically, in each round of training, we sequentially use the high-confidence prediction ratings (consistent ratings) of any two models as auxiliary training data for the remaining model, and the three models cooperatively improve recommendation robustness. Notably, TCD adds pseudo label data instead of deleting abnormal data, which avoids the cleaning of normal data, and the cooperative training of the three models is also beneficial to model generalization. Through extensive experiments with five poisoning attacks on three real-world datasets, the results show that the robustness improvement of TCD significantly outperforms baselines. It is worth mentioning that TCD is also beneficial for model generalizations.

14. FedGRec: Federated Graph Recommender System with Lazy Update of Latent  Embeddings

李俊义， 黄恒

https://arxiv.org/abs/2210.13686

推荐系统在工业中被广泛使用，以改善用户体验。尽管取得了巨大的成功，但他们最近因收集私人用户数据而受到批评。联邦学习 （FL） 是一种无需直接数据共享即可学习分布式数据的新范例。因此，建议使用联邦推荐器（FedRec）系统来减轻非分布式推荐系统的隐私问题。但是，FedRec系统与其非分布式系统存在性能差距。主要原因是本地客户端的用户-项目交互图不完整，因此FedRec系统无法很好地利用间接用户-项目交互。在本文中，我们提出了联邦图推荐系统（FedGRec）来缓解这一差距。我们的FedGRec系统可以有效地利用间接的用户-项目交互。更准确地说，在我们的系统中，用户和服务器显式存储用户和项目的潜在嵌入，其中潜在嵌入汇总了间接用户-项目交互的不同顺序，并在本地训练期间用作缺失交互图的代理。我们进行了广泛的实证评估，以验证使用潜在嵌入作为缺失交互图的代理的有效性;实验结果表明，与各种基线相比，我们的系统具有更好的性能。本文的简短版本以 https://federated-learning.org/fl-neurips-2022/

15. 基于会话的推荐系统图上的异构信息交叉

郑晓林， 吴睿， 韩忠轩， 陈朝超， 陈林迅， 韩冰

https://arxiv.org/abs/2210.12940

推荐系统是基本的信息过滤技术，用于推荐满足用户个性和潜在需求的内容或项目。作为解决用户识别困难和历史信息不可用的关键解决方案，基于会话的推荐系统提供仅依靠用户在当前会话中的行为的推荐服务。然而，大多数现有研究并没有很好地设计用于对异构用户行为进行建模并在实际场景中捕获它们之间的关系。为了填补这一空白，在本文中，我们提出了一种新的基于图的方法，即图上的异构信息交叉（HICG）。HICG利用会话中的多种用户行为来构建异构图，并通过有效地交叉图上的异构信息来捕捉用户当前的兴趣和他们的长期偏好。此外，我们还提出了一个名为HICG-CL的增强版本，它结合了对比学习（CL）技术来增强项目表示能力。通过利用不同会话之间的项目共现关系，HICG-CL 提高了 HICG 的推荐性能。我们在三个真实世界的推荐数据集上进行了广泛的实验，结果验证了（i）HICG通过在异构图上利用多种类型的行为实现了最先进的性能。（ii） HICG-CL通过拟议的对比学习模块进一步显著提高了HICG的推荐绩效。

16. 学习可转移顺序推荐器的向量量化项表示

侯宇鹏， 何占奎， 朱利安·麦考利， 赵鑫

https://arxiv.org/abs/2210.12316

最近，自然语言文本的通用性已被用于开发可转移的推荐系统。基本思想是使用预先训练的语言模型 （PLM） 将项目文本编码为项目表示形式。尽管具有可转移性，但项目文本和项目表示之间的绑定可能过于紧密，导致诸如过分强调文本相似性和夸大域差距等潜在问题。为了解决这个问题，本文提出了VQ-Rec，这是一种学习可转移顺序推荐器的向量量化项目表示的新方法。我们方法的主要新颖之处在于新的项目表示方案：它首先将项目文本映射到离散索引的向量（称为项目代码），然后使用这些索引来查找代码嵌入表以派生项目表示。这样的方案可以表示为“文本->代码->表示”。基于这种表示方案，我们进一步提出了一种增强的对比预训练方法，使用半合成和混合域代码表示作为硬否定。此外，我们设计了一种基于可微置换网络的跨域微调方法。在六个公共基准上进行的广泛实验证明了所提出的方法在跨领域和跨平台设置中的有效性。

17. 采用推荐系统支持数据和算法共享

彼得·穆尔纳、斯特凡·施梅尔达、迪特·泰勒、斯蒂芬妮·林施塔特、多米尼克·科瓦尔德

https://arxiv.org/abs/2210.11828

数据和算法共享是数据和人工智能驱动型经济的必要组成部分。数据和算法的有效共享依赖于用户、数据提供者和算法提供者之间的积极相互作用。尽管众所周知，推荐系统可以有效地将电子商务环境中的用户和项目互连起来，但缺乏对推荐系统在数据和算法共享方面的适用性的研究。为了填补这一空白，我们确定了六种支持数据和算法共享的推荐场景，其中四种场景与电子商务应用程序中的传统推荐场景有很大不同。我们使用基于OpenML数据和算法共享平台的交互数据的新型数据集来评估这些推荐场景，我们也为科学界提供了该数据集。具体而言，我们研究了三种类型的推荐方法，即受欢迎程度推荐、协作推荐和基于内容的推荐。我们发现，基于协作的建议在所有方案中都能提供最准确的建议。此外，推荐的准确性在很大程度上取决于特定场景，例如，针对用户的算法建议比针对数据集的算法建议更困难。最后，基于内容的方法生成了涵盖最多数据集和算法的流行度偏差最小的建议。