第33届国际人工智能联合会议（International Joint Conference on Artificial Intelligence, 简称为IJCAI）是人工智能领域顶级的国际学术会议之一，也是CCF-A类会议。今年的IJCAI将于2024年8月03-09日在韩国济州岛举办。在今年的5461篇投稿论文中，有799篇大约14.63%的论文被接收，其中跟推荐系统相关的论文大约16篇。另外，跟（大）语言模型相关的论文40篇。
https://ijcai24.org/main-track-accepted-papers/
通过对今年的论文题目进行分析发现，将已有主流技术应用在推荐系统领域的多个应用场景是这个会议的特色，比如股票投资推荐、兴趣点推荐、课程推荐、医疗推荐、社交推荐、会话推荐、旅行前城市推荐等。所利用的技术主要包括去偏、反事实、强化学习、超图神经网络、对比学习、度量学习、联邦学习、成员推理攻击、扩散模型、基础模型、自编码器、协同过滤等。
下文整理了上述分析的相关论文的标题以及摘要，感兴趣的可以深入阅读。
1. Automatic De-Biased Temporal-Relational Modeling for Stock Investment Recommendation
2. Counterfactual User Sequence Synthesis Augmented with Continuous Time Dynamic Preference Modeling for Sequential POI Recommendation
3. Hierarchical Reinforcement Learning on Multi-Channel Hypergraph Neural Network for Course Recommendation
4. R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-channel Information Fusion for Therapy Recommendation
5. Graph Attention Network with High-Order Neighbor Information Propagation for Social Recommendation
6. DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
7. Spatial-Temporal Perceiving: Deciphering User Hierarchical Intent in Session-Based Recommendation
8. KDDC: Knowledge-Driven Disentangled Causal Metric Learning for Pre-Travel Out-of-Town Recommendation
9. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning
10. Learning Fair Representations for Recommendation via Information Bottleneck Principle
11. Hierarchical Reinforcement Learning for Point of Interest Recommendation
12. Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought
13. SeeDRec: Sememe-based Diffusion for Sequential Recommendation
14. Federated Adaptation for Foundation Model-based Recommendations
15. Graph Collaborative Expert Finding with Contrastive Learning
16. SVD-AE: Simple Autoencoders for Collaborative Filtering
1. Automatic De-Biased Temporal-Relational Modeling for Stock Investment Recommendation
Weijun Chen, Shun Li, Xipu Yu, Heyuan Wang, Wei Chen, Tengjiao Wang
Stock investment recommendation is crucial for guiding investment decisions and managing portfolios. Recent studies have demonstrated the potential of temporal-relational models (TRM) to yield excess investment returns. However, in the complicated finance ecosystem, the current TRM suffer from both the intrinsic temporal bias from the low signal-to-noise ratio (SNR) and the relational bias caused by utilizing inappropriate relational topologies and propagation mechanisms. Moreover, the distribution shifts behind macro-market scenarios invalidate the underlying i.i.d. assumption and limit the generalization ability of TRM. In this paper, we pioneer the impact of the above issues on the effective learning of temporal-relational patterns and propose an Automatic De-Biased Temporal-Relational Model (ADB-TRM) for stock recommendation. Specifically, ADB-TRM consists of three main components, i.e., (i) a meta-learned architecture forms a dual-stage training process, with the inner part ameliorating temporal-relational bias and the outer meta-learner counteracting distribution shifts, (ii) automatic adversarial sample generation guides the model adaptively to alleviate bias and enhance its profiling ability through adversarial training, and (iii) global-local interaction helps seek relative invariant stock embeddings from local and global distribution perspectives to mitigate distribution shifts. Experiments on three datasets from distinct stock markets show that ADB-TRM excels state-of-the-arts over 28.41% and 9.53% in terms of cumulative and risk-adjusted returns.
2. Counterfactual User Sequence Synthesis Augmented with Continuous Time Dynamic Preference Modeling for Sequential POI Recommendation
Lianyong Qi, Yuwen Liu, Weiming Liu, Shichao Pei, Xiaolong Xu, Xuyun Zhang, Yingjie Wang, Wanchun Dou
With the proliferation of Location-based Social Networks (LBSNs), user check-in data at Points-of-Interest (POIs) has surged, offering rich insights into user preferences. However, sequential POI recommendation systems always face two pivotal challenges. A challenge lies in the difficulty of modeling time in a discrete space, which fails to accurately capture the dynamic nature of user preferences. Another challenge is the inherent sparsity and noise in continuous POI recommendation, which hinder the recommendation process. To address these challenges, we propose counterfactual user sequence synthesis with continuous time dynamic preference modeling (CussCtpm). CussCtpm innovatively combines Gated Recurrent Unit (GRU) with neural Ordinary Differential Equations (ODEs) to model user preferences in a continuous time framework. CussCtpm captures user preferences at both the POI-level and interest-level, identifying deterministic and non-deterministic preference concepts. Particularly at the interest-level, we employ GRU and neural ODEs to model users’ dynamic preferences in continuous space, aiming to capture finer-grained shifts in user preferences over time. Furthermore, CussCtpm utilizes counterfactual data augmentation to generate counterfactual positive and negative user sequences. Our extensive experiments on two widely-used public datasets demonstrate that CussCtpm outperforms several advanced baseline models.
3. Hierarchical Reinforcement Learning on Multi-Channel Hypergraph Neural Network for Course Recommendation
Lu Jiang, Yanan Xiao, Xinxin Zhao, Yuanbo Xu, Shuli Hu, Pengyang Wang, Minghao Yin
https://arxiv.org/abs/2405.14359
With the widespread popularity of massive open online courses, personalized course recommendation has become increasingly important due to enhancing users’ learning efficiency. While achieving promising performances, current works suffering from the vary across the users and other MOOC entities. To address this problem, we propose hierarchical reinforcement learning with a multi-channel hypergraphs neural network for course recommendation(called HHCoR). Specifically, we first construct an online course hypergraph as the environment to capture the complex relationships and historical information by considering all entities. Then, we design a multi-channel propagation mechanism to aggregate embeddings in the online course hypergraph and extract user interest through an attention layer. Besides, we employ two-level decision-making: the low-level focuses on the rating courses, while the high-level integrates these considerations to finalize the decision. Furthermore, in co-optimization, we design a joint reward function to improve the policy of two-layer agents. Finally, we conducted extensive experiments on two real-world datasets and the quantitative results have demonstrated the effectiveness of the proposed method.
4. R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-channel Information Fusion for Therapy Recommendation
Nengjun Zhu, Jieyun Huang, Jian Cao, Liang Hu, Zixuan Yuan, Huanjing Gao
Integrating data-driven and rule-based approaches is crucial for therapy recommendations since they can collaborate to achieve better performance. Medical rules, which are chains of reasoning that can infer therapies, widely exist. However, their symbolic and logical forms make integrating them with data-driven modeling technologies hard. Although rare attempts have indirectly modeled rules using data that supports them, the poor generalization of medical rules leads to inadequate supporting data and thus impairs the benefit of medical rules. To this end, we propose R2V-MIF, which fills the gap by rule-to-vector contrastive learning (R2V) and multi-channel information fusion (MIF). R2V is a data-free module and utilizes a hypergraph, including condition and result nodes, to instantiate the logic of medical rules. Each rule is reflected in the relations between nodes, and their representations are determined through contrastive learning. By taking rule representations as a bridge, MIF integrates the knowledge from medical rules, similar neighbors, and patient contents, and then recommends therapies. Extensive experiments show that R2V-MIF outperforms the baselines in several metrics using real-world medical data. Our code is available at https://github.com/vgeek-z/r2vmif.
5. Graph Attention Network with High-Order Neighbor Information Propagation for Social Recommendation
Fei Xiong, Haoran Sun, Guixun Luo, Shirui Pan, Meikang Qiu, Liang Wang
In recommender systems, graph neural networks (GNN) can integrate interactions between users and items with their attributes, which makes GNN-based methods more powerful. However, directly stacking multiple layers in a graph neural network can easily lead to over-smoothing, hence recommendation systems based on graph neural networks typically underutilize higher-order neighborhoods in their learning. Although some heterogeneous graph random walk methods based on meta-paths can achieve higher-order aggregation, the focus is predominantly on the nodes at the ends of the paths. Moreover, these methods require manually defined meta-paths, which limits the model’s expressiveness and flexibility. Furthermore, path encoding in graph neural networks usually focuses only on the sequence leading to the target node. However, real-world interactions often do not follow this strict sequence, limiting the predictive performance of sequence-based network models. These problems prevent GNN-based methods from being fully effective. We propose a Graph Attention network with Information Propagation path aggregation for Social Recommendation (GAIPSRec). Firstly, we propose a universal heterogeneous graph sampling framework that does not require manually defining meta-paths for path sampling, thereby offering greater flexibility. Moreover, our method takes into account all nodes on the aggregation path and is capable of learning information from higher-order neighbors without succumbing to over-smoothing. Finally, our method utilizes a gate mechanism to fuse sequential and non-sequential dependence in encoding path instances, allowing a more holistic view of the data. Extensive experiments on real-world datasets show that our proposed GAIPSRec improves the performance significantly and outperforms state-of-the-art methods.
6. DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Leilei Ding, Dazhong Shen, Chao Wang, Tianfu Wang, Le Zhang, Yanyong Zhang
https://arxiv.org/abs/2403.04287
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph’s node information and topology. However, these models often face the famous over-smoothing issue, leading to indistinct user and item embeddings and reduced personalization. Traditional desmoothing methods in GCN-based systems are model-specific, lacking a universal solution. This paper introduces a novel, model-agnostic approach named Desmoothing Framework for GCN-based Recommendation Systems (DGR). It effectively addresses over-smoothing on general GCN-based recommendation models by considering both global and local perspectives. Specifically, we first introduce vector perturbations during each message passing layer to penalize the tendency of node embeddings approximating overly to be similar with the guidance of the global topological structure. Meanwhile, we further develop a tailored-design loss term for the readout embeddings to preserve the local collaborative relations between users and their neighboring items. In particular, items that exhibit a high correlation with neighboring items are also incorporated to enhance the local topological information. To validate our approach, we conduct extensive experiments on 5 benchmark datasets based on 5 well-known GCN-based recommendation models, demonstrating the effectiveness and generalization of our proposed framework. Our code is available at https://github.com/me-sonandme/DGR
7. Spatial-Temporal Perceiving: Deciphering User Hierarchical Intent in Session-Based Recommendation
Xiao Wang, Tingting Dai, Qiao Liu, Shuang Liang
Session-based recommendation (SBR) aims to predict the next-interacted item based on anonymous users’ behavior sequences. The main challenge is how to recognize the user intent with limited interactions to achieve a more accurate inference of user behavior. Existing works usually regard several consecutive items in the current session as intent. However, we argue such intent generation based on temporal transition ignores the fact that each item also has its semantically connected items in the feature space, which can be regarded as spatial intent. The limited consideration of intent fails to capture complex behavioral patterns in real-world scenarios, leading to sub-optimal solutions. To address this issue, we propose the Hierarchical Intent Perceiving Contrastive Learning Framework (HearInt) for SBR, which proposes a hierarchical consideration of intents from both temporal and spatial perspective. Specifically, we first propose that the user’s temporal intents are mutually exclusive while the spatial intents are mutually compatible. Following these analyses, we design a Temporal Intent Decoupling module to mitigate the mutual influence of long-term and short-term intents, and a Cross-scale Contrastive Learning task to enhance the consistency of intents across different spatial scales. Experimental results on three real-world datasets exhibit that HearInt achieves state-of-the-art performance.
8. KDDC: Knowledge-Driven Disentangled Causal Metric Learning for Pre-Travel Out-of-Town Recommendation
Yinghui Liu, Guojiang Shen, Chengyong Cui, Zhenzhen Zhao, Xiao Han, Jiaxin Du, Xiangyu Zhao, Xiangjie
Pre-travel recommendation is developed to provide a variety of out-of-town Point-of-Interests (POIs) for users planning to travel away from their hometowns but have not yet decided on their destination. Existing out-of-town recommender systems work on constructing users’ latent preferences and inferring travel intentions from their check-in sequences. However, there are still two challenges that hamper the performance of these approaches: i) Users’ interactive data (including hometown and out-of-town check-ins) tend to be rare, and while candidate POIs that come from different regions contain various semantic information; ii) The causes for user check-in include not only interest but also conformity, which are easily entangled and overlooked. To fill these gaps, we propose a Knowledge-Driven Disentangled Causal metric learning framework (KDDC) that mitigates interaction data sparsity by enhancing POI semantic representation and considers the distributions of two causes (i.e., conformity and interest) for pre-travel recommendation. Specifically, we pretrain a constructed POI attribute knowledge graph through a segmented interaction method and POI semantic information is aggregated via relational heterogeneity. In addition, we devise a disentangled causal metric learning to model and infer userrelated representations. Extensive experiments on two real-world nationwide datasets display the consistent superiority of our KDDC over state-of-theart baselines.
9. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning
Zhenghong Lin, Wei Huang, Hengyu Zhang, Jiayu Xu, Weiming Liu, Xingting Liao, Fan Wang, Shiping Wang, Yanchao Tan
Recently, dual-target cross-domain recommendation (DTCDR) has been proposed to alleviate the data sparsity problem by sharing the common knowledge across domains simultaneously. However, existing methods often assume that personal data containing abundant identifiable information can be directly accessed, which results in a controversial privacy leakage problem of DTCDR. To this end, we introduce the P2DTR framework, a novel approach in DTCDR while protecting private user information. Specifically, we first design a novel inter-client knowledge extraction mechanism, which exploits the private set intersection algorithm and prototype-based federated learning to enable collaboratively modeling among multiple users and a server. Furthermore, to improve the recommendation performance based on the extracted common knowledge across domains, we proposed an intra-client enhanced recommendation, consisting of a constrained dominant set (CDS) propagation mechanism and dual-recommendation module. Extensive experiments on real-world datasets validate that our proposed P2DTR framework achieves superior utility under a privacy-preserving guarantee on both domains.
10. Learning Fair Representations for Recommendation via Information Bottleneck Principle
Junsong Xie, Yonghui Yang, Zihan Wang, Le Wu
User-oriented recommender systems (RS) characterize users’ preferences based on observed behaviors and are widely deployed in personalized services. However, RS may unintentionally capture biases related to sensitive attributes (e.g., gender) from behavioral data, leading to unfair issues and discrimination against particular groups (e.g., females). Adversarial training is a popular technique for fairness-aware RS, when filtering sensitive information in user modeling. Despite advancements in fairness, achieving a good accuracy-fairness trade-off remains a challenge in adversarial training. In this paper, we investigate fair representation learning from a novel information theory perspective. Specifically, we propose a model-agnostic Fair recommendation method via the Information Bottleneck principle FairIB. The learning objective of FairIB is to maximize the mutual information between user representations and observed interactions, while simultaneously minimizing it between user representations and sensitive attributes. This approach facilitates the capturing of essential collaborative signals in user representations while mitigating the inclusion of unnecessary sensitive information. Empirical studies on two real-world datasets demonstrate the effectiveness of the proposed FairIB, which significantly improves fairness while maintaining competitive recommendation accuracy, either in single or multiple sensitive scenarios. The code is available at https://github.com/jsxie9/IJCAI_FairIB.
11. Hierarchical Reinforcement Learning for Point of Interest Recommendation
Yanan Xiao, Lu Jiang, Kunpeng Liu, Yuanbo Xu, Pengyang Wang, Minghao Yin
With the increasing popularity of location-based services, accurately recommending points of interest (POIs) has become a critical task. Although existing technologies are proficient in processing time-series data, they fall short when it comes to accommodating the diversity and dynamism in users’ POI selections, particularly in extracting key signals from complex historical behaviors. To address this challenge, we introduced the Hierarchical Reinforcement Learning Preprocessing Framework (HRL-PRP), a framework that can be integrated into existing recommendation models to effectively optimize user profiles. The HRL-PRP framework employs a two-tiered decision-making process, where the high-level process determines the necessity of modifying profiles, and the low-level process focuses on selecting POIs within the profiles. Through evaluations on multiple real-world datasets, we have demonstrated that HRL-PRP surpasses existing state-of-the-art methods in various recommendation performance metrics.
12. Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought
Xiaoxiao Chi, Xuyun Zhang, Yan Wang, Lianyong Qi, Amin Beheshti, Xiaolong Xu, Kim-Kwang Raymond Choo, Shuo Wang, Hongsheng Hu
https://arxiv.org/abs/2405.07018
Recommender systems have been successfully applied in many applications. Nonetheless, recent studies demonstrate that recommender systems are vulnerable to membership inference attacks (MIAs), leading to the leakage of users’ membership privacy. However, existing MIAs relying on shadow training suffer a large performance drop when the attacker lacks knowledge of the training data distribution and the model architecture of the target recommender system. To better understand the privacy risks of recommender systems, we propose shadow-free MIAs that directly leverage a user’s recommendations for membership inference. Without shadow training, the proposed attack can conduct MIAs efficiently and effectively under a practice scenario where the attacker is given only black-box access to the target recommender system. The proposed attack leverages an intuition that the recommender system personalizes a user’s recommendations if his historical interactions are used by it. Thus, an attacker can infer membership privacy by determining whether the recommendations are more similar to the interactions or the general popular items. We conduct extensive experiments on benchmark datasets across various recommender systems. Remarkably, our attack achieves far better attack accuracy with low false positive rates than baselines while with a much lower computational cost.
13. SeeDRec: Sememe-based Diffusion for Sequential Recommendation
Haokai Ma, Ruobing Xie, Lei Meng, Yimeng Yang, Xingwu Sun, Zhanhui Kang
Inspired by the power of Diffusion Models (DM) verified in various fields, some pioneering works have started to explore DM in recommendation. However, these prevailing endeavors commonly implement diffusion on item indices, leading to the increasing time complexity, the lack of transferability, and the inability to fully harness item semantic information. To tackle these challenges, we propose SeeDRec, a sememe-based diffusion framework for sequential recommendation (SR). Specifically, inspired by the notion of sememe in NLP, SeeDRec first defines a similar concept of recommendation sememe to represent the minimal interest unit and upgrades the diffusion objective from the item level to the sememe level. With the Sememe-to-Interest Diffusion Model (S2IDM), SeeDRec can accurately capture the user’s diffused interest distribution learned from both local interest evolution and global interest generalization while maintaining low computational costs. Subsequently, an Interest-aware Prompt-enhanced (IPE) strategy is proposed to better guide each user’s sequential behavior modeling via the learned user interest distribution. Extensive experiments on nine SR datasets and four cross-domain SR datasets verify its effectiveness and universality. The code will be released upon acceptance.
14. Federated Adaptation for Foundation Model-based Recommendations
Chunxu Zhang, Guodong Long, Guo Hongkuan, Xiao Fang, Yang Song, Zhaojie Liu, Guorui Zhou, Zijian Zhang, Yang Liu, Bo Yang
https://arxiv.org/abs/2405.04840
With the recent success of large language models, particularly foundation models with generalization abilities, applying foundation models for recommendations becomes a new paradigm to improve existing recommendation systems. It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy. This paper proposes a novel federated adaptation mechanism to enhance the foundation model-based recommendation system in a privacy-preserving manner. Specifically, each client will learn a lightweight personalized adapter using its private data. The adapter then collaborates with pre-trained foundation models to provide recommendation service efficiently with fine-grained manners. Importantly, users’ private behavioral data remains secure as it is not shared with the server. This data localization-based privacy preservation is embodied via the federated learning framework. The model can ensure that shared knowledge is incorporated into all adapters while simultaneously preserving each user’s personal preferences. Experimental results on four benchmark datasets demonstrate our method’s superior performance. Implementation code is available at https://github.com/Zhangcx19/IJCAI-24-FedPA
15. Graph Collaborative Expert Finding with Contrastive Learning
Qiyao Peng, Wenjun Wang, Hongtao Liu, Cuiying Huo, Minglai Shao
In Community Question Answering (CQA) websites, most current expert finding methods often model expert embeddings from textual features and optimize them with expert-question first-order interactions, i.e., this expert has answered this question. In this paper, we try to address the limitation of current models that typically neglect the intrinsic high-order connectivity within expert-question interactions, which is pivotal for collaborative effects. We introduce an innovative and simple approach: by conceptualizing expert-question interactions as a bipartite graph, and then we propose a novel graph-based expert finding method based on contrastive learning to effectively capture both first-order and intricate high-order connectivity, named CGEF. Specifically, we employ a question encoder to model questions from titles and employ the graph attention network to recursively propagate embeddings. Besides, to alleviate the problem of sparse interactions, we devise two auxiliary tasks to enhance expert modeling. First, we generate multiple views of one expert, including: 1) behavior-level augmentation drops interaction edges randomly in the graph; 2) interest-level augmentation randomly replaces question titles with tags in the graph. Then we maximize the agreement between one expert and the corresponding augmented expert on a specific view. In this way, the model can effectively inject collaborative signals into expert modeling. Extensive experiments on six CQA datasets demonstrate significant improvements compared with recent methods.
16. SVD-AE: Simple Autoencoders for Collaborative Filtering
Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee, Srijan Kumar, Noseong Park
https://arxiv.org/abs/2405.04746
Collaborative filtering methods for recommendation systems have been extensively researched, ranging from matrix factorization and autoencoder-based methods to graph filtering-based methods. In particular, lightweight methods that require almost no training have been recently proposed to reduce the overall computation – for example, designing a linear autoencoder model using a closed-form solution. Despite their successes, existing methods include heuristic techniques and still have room to improve the trade-offs among accuracy, efficiency, and robustness. In particular, there are no well-designed closed-form studies for balanced collaborative filtering in terms of the aforementioned trade-offs. In this paper, we design SVD-AE, a simple yet effective singular vector decomposition (SVD)-based linear autoencoder, whose closed-form solution can be defined based on SVD, for collaborative filtering. Since its closed-form solution can be calculated at once, our proposed method does not involve any iterative training processes. Furthermore, given the noisy nature of the rating matrix, we explore the robustness against such noisy interactions of existing collaborative filtering methods and our SVD-AE. As a result, we demonstrate that our simple design choice based on truncated SVD can be used to strengthen the noise robustness of the recommendation while improving efficiency. In the end, we conclude that our method offers the best overall balance among the recommendation accuracy, computation time, and robustness. Code is available at https://github.com/seoyoungh/svd-ae
