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- research-articleJune 2024
Curriculum disentangled recommendation with noisy multi-feedback
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing SystemsDecember 2021, Article No.: 2062, Pages 26924–26936Learning disentangled representations for user intentions from multi-feedback (i.e., positive and negative feedback) can enhance the accuracy and explainability of recommendation algorithms. However, learning such disentangled representations from multi-...
- research-articleMay 2024
Multimodal Conditioned Diffusion Model for Recommendation
WWW '24: Companion Proceedings of the ACM on Web Conference 2024May 2024, Pages 1733–1740https://doi.org/10.1145/3589335.3651956Multimodal recommendation aims at to modeling the feature distributions of items by using their multi-modal information. Prior efforts typically focus on the denoising of the user-item graph with a degree-sensitive strategy, which may not well-handle the ...
- research-articleMay 2024
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
WWW '24: Proceedings of the ACM on Web Conference 2024May 2024, Pages 3679–3689https://doi.org/10.1145/3589334.3645537Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies mainly focus on simulating human dialogue. Human non-verbal behaviors, such ...
- ArticleMarch 2024
Large Language Models are Zero-Shot Rankers for Recommender Systems
Advances in Information RetrievalMar 2024, Pages 364–381https://doi.org/10.1007/978-3-031-56060-6_24AbstractRecently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks. Along this line of research, this work aims to investigate the ...
- research-articleMarch 2024
Group-to-group recommendation with neural graph matching
AbstractNowadays, with the development of recommender systems, an emerging recommendation scenario called group-to-group recommendation has played a vital role in information acquisition for users. The new recommendation scenario seeks to recommend a ...
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- research-articleFebruary 2024
FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 4Article No.: 94, Pages 1–25https://doi.org/10.1145/3638352The emergence of Graph Neural Networks (GNNs) has greatly advanced the development of recommendation systems. Recently, many researchers have leveraged GNN-based models to learn fair representations for users and items. However, current GNN-based models ...
- research-articleFebruary 2024
Triple Sequence Learning for Cross-domain Recommendation
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 4Article No.: 91, Pages 1–29https://doi.org/10.1145/3638351Cross-domain recommendation (CDR) aims at leveraging the correlation of users’ behaviors in both the source and target domains to improve the user preference modeling in the target domain. Conventional CDR methods typically explore the dual-relations ...
- ArticleSeptember 2023
Future Augmentation with Self-distillation in Recommendation
- Chong Liu,
- Ruobing Xie,
- Xiaoyang Liu,
- Pinzheng Wang,
- Rongqin Zheng,
- Lixin Zhang,
- Juntao Li,
- Feng Xia,
- Leyu Lin
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo TrackSep 2023, Pages 602–618https://doi.org/10.1007/978-3-031-43427-3_36AbstractSequential recommendation (SR) aims to provide appropriate items a user will click according to the user’s historical behavior sequence. Conventional SR models are trained under the next item prediction task, and thus should deal with two ...
- short-paperSeptember 2023
Generative Next-Basket Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023, Pages 737–743https://doi.org/10.1145/3604915.3608823Next-basket Recommendation (NBR) refers to the task of predicting a set of items that a user will purchase in the next basket. However, most of existing works merely focus on the correlations between user preferences and predicted items, ignoring the ...
- short-paperSeptember 2023
Interpretable User Retention Modeling in Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023, Pages 702–708https://doi.org/10.1145/3604915.3608818Recommendation usually focuses on immediate accuracy metrics like CTR as training objectives. User retention rate, which reflects the percentage of today’s users that will return to the recommender system in the next few days, should be paid more ...
- research-articleSeptember 2023
Exploring False Hard Negative Sample in Cross-Domain Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023, Pages 502–514https://doi.org/10.1145/3604915.3608791Negative Sampling in recommendation aims to capture informative negative instances for the sparse user-item interactions to improve the performance. Conventional negative sampling methods tend to select informative hard negative samples (HNS) besides ...
- short-paperJuly 2023
Attacking Pre-trained Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 1811–1815https://doi.org/10.1145/3539618.3591949Recently, 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. ...
- research-articleJuly 2023
Negative Can Be Positive: Signed Graph Neural Networks for Recommendation
Information Processing and Management: an International Journal (IPRM), Volume 60, Issue 4Jul 2023https://doi.org/10.1016/j.ipm.2023.103403AbstractMost of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender ...
Highlights- In this paper, we study the negative feedback in the recommender system, which is of great importance.
- research-articleMay 2023
Connecting Embeddings Based on Multiplex Relational Graph Attention Networks for Knowledge Graph Entity Typing
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 5May 2023, Pages 4608–4620https://doi.org/10.1109/TKDE.2022.3142056Knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs, which is a significant subtask of KG completion. Despite of its progress, however, we observe that it still faces two non-trivial challenges: (i) most existing KGET ...
- short-paperApril 2023
Reweighting Clicks with Dwell Time in Recommendation
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023April 2023, Pages 341–345https://doi.org/10.1145/3543873.3584624The click behavior is the most widely-used user positive feedback in recommendation. However, simply considering each click equally in training may suffer from clickbaits and title-content mismatching, and thus fail to precisely capture users’ real ...
- research-articleApril 2023
Cross-domain Recommendation with Behavioral Importance Perception
WWW '23: Proceedings of the ACM Web Conference 2023April 2023, Pages 1294–1304https://doi.org/10.1145/3543507.3583494Cross-domain recommendation (CDR) aims to leverage the source domain information to provide better recommendation for the target domain, which is widely adopted in recommender systems to alleviate the data sparsity and cold-start problems. However, ...
- ArticleApril 2023
Multi-granularity Item-Based Contrastive Recommendation
Database Systems for Advanced ApplicationsApr 2023, Pages 406–416https://doi.org/10.1007/978-3-031-30672-3_27AbstractContrastive learning (CL) has shown its power in recommendation. However, most CL-based models build their CL tasks merely focusing on the user’s aspects, ignoring systematically modeling the rich and diverse information in items. In this work, we ...
- ArticleApril 2023
Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation
Database Systems for Advanced ApplicationsApr 2023, Pages 373–388https://doi.org/10.1007/978-3-031-30672-3_25AbstractRecently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist ...
- ArticleMarch 2023
Customized Conversational Recommender Systems
Machine Learning and Knowledge Discovery in DatabasesSep 2022, Pages 740–756https://doi.org/10.1007/978-3-031-26390-3_43AbstractConversational recommender systems (CRS) aim to capture user’s current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-machine interactive system, it is essential for CRS to improve the ...
- research-articleFebruary 2023
Visually grounded commonsense knowledge acquisition
- Yuan Yao,
- Tianyu Yu,
- Ao Zhang,
- Mengdi Li,
- Ruobing Xie,
- Cornelius Weber,
- Zhiyuan Liu,
- Hai-Tao Zheng,
- Stefan Wermter,
- Tat-Seng Chua,
- Maosong Sun
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligenceFebruary 2023, Article No.: 740, Pages 6583–6592https://doi.org/10.1609/aaai.v37i5.25809Large-scale commonsense knowledge bases empower a broad range of AI applications, where the automatic extraction of commonsense knowledge (CKE) is a fundamental and challenging problem. CKE from text is known for suffering from the inherent sparsity and ...