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- A Survey on Causal Inference720
This survey provides a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks, and presents the plausible applications of these methods, including the applications in advertising, recommendation, medicine, and so on.
- RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms658
A unified, comprehensive and efficient recommender system library called RecBole (pronounced as [rEk'boUl@r]), which provides a unified framework to develop and reproduce recommendation algorithms for research purpose and provides a series of auxiliary functions, tools, and scripts to facilitate the use of this library.
- Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation527
This paper proposes to resolve conflicts among multiple sources of heterogeneous data types by using an optimization framework where truths and source reliability are defined as two sets of unknown variables and the objective is to minimize the overall weighted deviation between the truths and the multi-source observations.
- A Survey on Truth Discovery460
This survey focuses on providing a comprehensive overview of truth discovery methods, and summarizing them from different aspects, and offers some guidelines on how to apply these approaches in application domains.
- Towards Universal Sequence Representation Learning for Recommender Systems400
A novel universal sequence representation learning approach that utilizes the associated description text of items to learn transferable representations across different recommendation scenarios, and leads to a performance improvement in a cross-platform setting, showing the strong transferability of the proposed universal SRL method.
- A confidence-aware approach for truth discovery on long-tail data358
A confidence-aware truth discovery (CATD) method to automatically detect truths from conflicting data with long-tail phenomenon is proposed, which outperforms existing state-of-the-art truth discovery approaches by successful discounting the effect of small sources.
- Representation Learning for Treatment Effect Estimation from Observational Data357
A local similarity preserved individual treatment effect (SITE) estimation method based on deep representation learning that preserves local similarity and balances data distributions simultaneously, by focusing on several hard samples in each mini-batch.
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- FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning302
This paper builds a complete end-to-end benchmarking pipeline under real-world scenarios, automizing the processes of dataset preprocessing, federated fine-tuning execution or simulation, and performance evaluation, and provides comprehensive and off-the-shelf federated parameter-efficient fine-tuning (PEFT) algorithm implementations and versatile programming interfaces for future extension.
- Joint Slot Filling and Intent Detection via Capsule Neural Networks259
A capsule-based neural network model is proposed which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema and a re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation.
- FaitCrowd199
Experimental results on two real-world datasets show that FaitCrowd can significantly reduce the error rate of aggregation compared with the state-of-the-art multi-source aggregation approaches due to its ability of learning topical expertise from question content and collected answers.
- Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources180
FlexLoRA is introduced, a simple yet effective aggregation scheme for LLM fine-tuning, which mitigates the ``bucket effect'' in traditional FL that restricts the potential of clients with ample resources by tying them to the capabilities of the least-resourced participants.
- Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning169
A dynamic weighted graph based RL method to learn a policy to select the action at each conversation turn, either asking an attribute or recommending items, which significantly outperforms state-of-the-art methods but also enhances the scalability and stability of CRS.
- Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems168
This paper proposes a novel cloud-enabled privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users' sensory data but also their reliability scores derived by the truth discovery approaches.
- FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model165
FedBiOT is introduced, a resource-efficient LLM fine-tuning approach to FL that conducts extensive experiments on LLaMA-2, empirically showing that the adapter has exceptional performance when reintegrated into the global LLM.
- Differential Privacy for Text Analytics via Natural Text Sanitization157
This paper takes a direct approach to text sanitization, to consider both sensitivity and similarity via the new local DP notion, enabling privacy-preserving natural language processing over the BERT language model with promising utility.
- Truth Discovery on Crowd Sensing of Correlated Entities154
Experiments on real-world crowd sensing applications show the advantages of the proposed method on discovering truths from conflicting information reported on correlated entities.
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- On the Discovery of Evolving Truth151
This work investigates the temporal relations among both object truths and source reliability, and proposes an incremental truth discovery framework that can dynamically update object truth and source weights upon the arrival of new data.
- Crowdsourcing for Multiple-Choice Question Answering149
A crowdsourced system for playing the “Who wants to be a millionaire?” quiz show is developed and deployed, and novel weighted aggregation schemes for aggregating the answers obtained from the crowd are investigated.
- Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering142
This paper presents a novel GNN-based CF model, named Robust Graph Collaborative Filtering (RGCF), to denoise unreliable interactions for recommendation and achieves significant improvement compared with baseline models.
- FederatedScope: A Flexible Federated Learning Platform for Heterogeneity126
A novel FL platform, named FederatedScope, is proposed, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants, and enables rich types of plug-in operations and components for efficient further development.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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