Bo Chen
Tenured Track Associate Professor
Builds networked and machine learning systems for immersive computing, with device-edge-cloud collaboration for visual, XR and 3D content applications.
Bo Chen's lab at SJTU works on distributed, cloud & edge systems, VR, AR & immersive media and generative 3D & visual content. 52 papers since 2023, 35 at top venues, mostly KDD and CIKM. Applications close .
At a glance
- papers, 2023–2026
- 14/ year
- at T1 venues
- 67%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage and DBLP, 2023–2026
- 10 papers2026 · T1 4 · collecting
- 10 papers2025 · T1 7
- 20 papers2024 · T1 12
- 12 papers2023 · T1 12
T1 venuesOtherVenues: KDD, CIKM, SIGIR, WWW, RecSys
Research focuspaper titles that use each topic's words
- Distributed, cloud & edge systems9 papers
- Generative 3D & visual content4 papers
- VR, AR & immersive media2 papers
- immersive2 papers
- networked0 papers
Paper list
From the lab homepage and DBLP.
202610 papers
- Trans. Recomm. Syst.CTRL: Connect Collaborative and Language Model for CTR Prediction (opens in a new tab)
- KDD (T1)DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark (opens in a new tab)
- ACM Trans. Inf. Syst.EENet: An Efficient and Effective Network for Large-Scale CTR Prediction (opens in a new tab)
- Trans. Recomm. Syst.Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models (opens in a new tab)
- Trans. Recomm. Syst.Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation (opens in a new tab)
- DASFAAGenerative Representational Learning of Foundation Models for Recommendation (opens in a new tab)
- SIGIR (T1)Modular Representation Compression: Adapting LLM Representations for Efficient and Effective Recommendation (opens in a new tab)
- KDD (T1)SMES: Towards Scalable Multi-Task Recommendation via Expert Sparsity (opens in a new tab)
- KDD (T1)Tutorial on Generative Recommendation: Foundations and Frontiers (opens in a new tab)
- Findings of ACLUnleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (opens in a new tab)
202510 papers
- SIGIR (T1)Action First: Leveraging Preference-Aware Actions for More Effective Decision-Making in Interactive Recommender Systems (opens in a new tab)
- KDD (T1)An Automatic Graph Construction Framework based on Large Language Models for Recommendation (opens in a new tab)
- SIGIR (T1)Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding (opens in a new tab)
- ACM Trans. Inf. Syst.How Can Recommender Systems Benefit from Large Language Models: A Survey (opens in a new tab)
- WWW (T1)Joint Modeling in Deep Recommender Systems (opens in a new tab)
- Frontiers Comput. Sci.Large language models make sample-efficient recommender systems (opens in a new tab)
- WWW (T1)LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations (opens in a new tab)
- KDD (T1)LLM4Tag: Automatic Tagging System for Information Retrieval via Large Language Models (opens in a new tab)
- Frontiers Comput. Sci.Towards efficient and effective unlearning of large language models for recommendation (opens in a new tab)
- Interspeech (T1)Vela: Scalable Embeddings with Voice Large Language Models for Multimodal Retrieval (opens in a new tab)
202420 papers
- Trans. Recomm. Syst.A Comprehensive Survey on Automated Machine Learning for Recommendations (opens in a new tab)
- ACM Trans. Knowl. Discov. DataA Survey on Bid Optimization in Real-Time Bidding Display Advertising (opens in a new tab)
- RecSys (T1)AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising (opens in a new tab)
- Knowl. Inf. Syst.AutoAssign+: Automatic Shared Embedding Assignment in streaming recommendation (opens in a new tab)
- WWW (T1)ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction (opens in a new tab)
- WSDM (T1)Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation (opens in a new tab)
- KDD (T1)DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation (opens in a new tab)
- CIKM (T1)ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation (opens in a new tab)
- KDD (T1)ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems (opens in a new tab)
- RecSys (T1)FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction (opens in a new tab)
- CIKM (T1)HierRec: Scenario-Aware Hierarchical Modeling for Multi-scenario Recommendations (opens in a new tab)
- —ImmerScope: Multi-view Video Aggregation at Edge towards Immersive Content Services (opens in a new tab)
202312 papers
- TKDE (T1)AIM: Automatic Interaction Machine for Click-Through Rate Prediction (opens in a new tab)
- WSDM (T1)AutoGen: An Automated Dynamic Model Generation Framework for Recommender System (opens in a new tab)
- WSDM (T1)AutoML for Deep Recommender Systems: Fundamentals and Advances (opens in a new tab)
- RecSys (T1)AutoOpt: Automatic Hyperparameter Scheduling and Optimization for Deep Click-through Rate Prediction (opens in a new tab)
- SIGIR (T1)AutoTransfer: Instance Transfer for Cross-Domain Recommendations (opens in a new tab)
- WWW (T1)Compressed Interaction Graph based Framework for Multi-behavior Recommendation (opens in a new tab)
- KDD (T1)Deep Landscape Forecasting in Multi-Slot Real-Time Bidding (opens in a new tab)
- CIKM (T1)DFFM: Domain Facilitated Feature Modeling for CTR Prediction (opens in a new tab)
- CIKM (T1)HAMUR: Hyper Adapter for Multi-Domain Recommendation (opens in a new tab)
- CIKM (T1)Optimal Real-Time Bidding Strategy for Position Auctions in Online Advertising (opens in a new tab)
- SIGIR (T1)PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations (opens in a new tab)
- SIGIR (T1)Single-shot Feature Selection for Multi-task Recommendations (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to SJTUFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- Members
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