Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jiankai Sun
dblp:121/4211
· DBLP profile ↗
35ranked-venue papers
11as first author
22since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 26 · 9 first-author · 18 since 2021Databases, data management, data science and information retrieval · 13 · 3 first-author · 6 since 2021Graphics, computer vision, multimedia, augmented reality and games · 8 · 6 first-author · 4 since 2021Systems, architecture and hardware · 4 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 3 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
14 papers |
3D vision · 30% Graph learning · 15% Deep learning architectures and training · 14% | |
| Databases, data mining, and information retrieval
5 papers |
Recommender systems · 62% Information retrieval · 22% Data mining · 12% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 50% Wearable and physiological sensing · 25% Health and well-being technologies · 25% | |
| Network and information security
2 papers |
Privacy and data protection · 100% |
Topics — the 30 heaviest of 60, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
camera pose estimation |
1.2 | 2 | 2025 | NopeRoomGS: Indoor 3D Gaussian Splatting Optimization without Camera Pose Input · NeurIPS 2025 CamNet: Coarse-to-Fine Retrieval for Camera Re-Localization · ICCV 2019 |
Human-robot interaction
assistive robotics |
1.0 | 1 | 2026 | Wearable Intelligence for Healthcare Robotics: From Brain Activity to Body Movements · AAAI 2026 |
Human-robot interaction
healthcare robotics |
1.0 | 1 | 2026 | Wearable Intelligence for Healthcare Robotics: From Brain Activity to Body Movements · AAAI 2026 |
Wearable and physiological sensing › multimodal sensing
multimodal wearable sensing |
1.0 | 1 | 2026 | Wearable Intelligence for Healthcare Robotics: From Brain Activity to Body Movements · AAAI 2026 |
Health and well-being technologies › rehabilitation technology
rehabilitation robotics |
1.0 | 1 | 2026 | Wearable Intelligence for Healthcare Robotics: From Brain Activity to Body Movements · AAAI 2026 |
Recommender systems
collaborative filtering |
0.9 | 2 | 2023 | Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems · KDD 2023 Learning to Recommend Accurate and Diverse Items · WWW 2017 |
Recommender systems
diversified recommendation |
0.9 | 2 | 2023 | DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation · WSDM 2023 Learning to Recommend Accurate and Diverse Items · WWW 2017 |
Computer vision › 3D vision › neural rendering
3d gaussian splatting |
0.9 | 1 | 2025 | NopeRoomGS: Indoor 3D Gaussian Splatting Optimization without Camera Pose Input · NeurIPS 2025 |
Computer vision › 3D vision
3d reconstruction |
0.9 | 1 | 2025 | NopeRoomGS: Indoor 3D Gaussian Splatting Optimization without Camera Pose Input · NeurIPS 2025 |
Natural language and speech › Language models and text generation › decoding
efficient decoding |
0.9 | 1 | 2025 | Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation · NeurIPS 2025 |
Natural language and speech › Language models and text generation › compositional generalization › length generalization
length extrapolation |
0.9 | 1 | 2025 | DAPE V2: Process Attention Score as Feature Map for Length Extrapolation · ACL (1) 2025 |
Computer vision › 3D vision › 3d reconstruction › uncalibrated reconstruction
pose-free reconstruction |
0.9 | 1 | 2025 | NopeRoomGS: Indoor 3D Gaussian Splatting Optimization without Camera Pose Input · NeurIPS 2025 |
Machine learning › Deep learning architectures and training
state space model |
0.9 | 1 | 2025 | Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation · NeurIPS 2025 |
Machine learning › Deep learning architectures and training
transformer |
0.9 | 1 | 2025 | DAPE V2: Process Attention Score as Feature Map for Length Extrapolation · ACL (1) 2025 |
Machine learning › Graph learning › network embedding
directed graph embedding |
0.8 | 2 | 2019 | ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation · AAAI 2019 Symmetrization for Embedding Directed Graphs · AAAI 2019 |
Robotics › Motion planning and robot control › robot learning › robot policy learning
generalist robot policy |
0.8 | 1 | 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 |
Machine learning › Graph learning
network embedding |
0.8 | 2 | 2019 | ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation · AAAI 2019 Symmetrization for Embedding Directed Graphs · AAAI 2019 |
Robotics › Motion planning and robot control
robot learning |
0.8 | 1 | 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 |
Machine learning › Trustworthy machine learning
fairness |
0.7 | 1 | 2023 | Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes · ICML 2023 |
Machine learning › Efficient and distributed learning › federated learning
federated evaluation |
0.7 | 1 | 2023 | DPAUC: Differentially Private AUC Computation in Federated Learning · AAAI 2023 |
Machine learning › Efficient and distributed learning
federated learning |
0.7 | 1 | 2023 | DPAUC: Differentially Private AUC Computation in Federated Learning · AAAI 2023 |
Machine learning › Graph learning
graph neural network |
0.7 | 1 | 2023 | DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation · WSDM 2023 |
Machine learning › Reinforcement learning
model-based reinforcement learning |
0.7 | 1 | 2023 | Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model · NeurIPS 2023 |
Machine learning › Reinforcement learning
offline reinforcement learning |
0.7 | 1 | 2023 | Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model · NeurIPS 2023 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
planning under uncertainty |
0.7 | 1 | 2023 | Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model · NeurIPS 2023 |
Recommender systems
graph-based recommendation |
0.7 | 1 | 2023 | DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation · WSDM 2023 |
Data mining › sampling
subsampling |
0.7 | 1 | 2023 | Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems · KDD 2023 |
Privacy and data protection
differential privacy |
0.7 | 1 | 2023 | DPAUC: Differentially Private AUC Computation in Federated Learning · AAAI 2023 |
Privacy and data protection › differential privacy › relaxed differential privacy
label differential privacy |
0.7 | 1 | 2023 | DPAUC: Differentially Private AUC Computation in Federated Learning · AAAI 2023 |
Privacy and data protection › information leakage
label leakage |
0.6 | 1 | 2022 | Label Leakage and Protection in Two-party Split Learning · ICLR 2022 |
Methods — techniques the papers use, named apart from their topics
AUC computation · 1.3uncertainty quantification · 1.0large-scale simulation · 1.0foundation models · 1.0neural geometric representation · 0.9local-to-global optimization · 0.9gated memory unit · 0.9differential attention · 0.9depth prior · 0.9convolutional data-adaptive position encoding · 0.9convolution on attention scores · 0.9transformer policy · 0.8large-scale pretraining · 0.8matrix factorization · 0.7submodular neighbor selection · 0.7model-agnostic subsampling · 0.7loss reweighting · 0.7layer attention · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Wearable Intelligence for Healthcare Robotics: From Brain Activity to Body MovementsabstractMy research aims to pioneer efficient and reliable wearable intelligence algorithms that transform healthcare robotics into adaptive, patient-centered systems. I take a four-step approach: (1) design multimodal wearable sensing platforms to capture human and biometric signals; (2) train a foundation model that learns from these rich datasets to reason about human behaviors and health states; (3) validate the model through large-scale simulation and principled uncertainty quantification; and (4) deploy it in rehabilitation and assistive robots for intelligent, personalized care. This research not only advances fundamental understanding of multimodal human behavior, but also opens new pathways for early disease diagnosis, adaptive treatment, and accessible digital health. By bridging AI, wearables, and robotics, my work aspires to lay the groundwork for the next generation of healthcare technologies that are proactive, trustworthy, and deeply aligned with human well-being. Jiankai Sun |
AAAI | 1 |
| 2025 | DAPE V2: Process Attention Score as Feature Map for Length ExtrapolationabstractThe attention mechanism is a fundamental component of the Transformer model, contributing to interactions among distinct tokens. In general, the attention scores are determined simply by the key-query products. However, this work’s occasional trial (combining DAPE and NoPE) of including additional MLPs on attention scores without position encoding indicates that the classical key-query multiplication may limit the performance of Transformers. In this work, we conceptualize attention as a feature map and apply the convolution operator (for neighboring attention scores across different heads) to mimic the processing methods in computer vision. Specifically, the main contribution of this paper is identifying and interpreting the Transformer length extrapolation problem as a result of the limited expressiveness of the naive query and key dot product, and we successfully translate the length extrapolation issue into a well-understood feature map processing problem, which is called Convolutional Data-Adaptive Position Encoding (CDAPE).The novel insight, which can be adapted to various attention-related models, reveals that the current Transformer architecture has the potential for further evolution. Extensive experiments demonstrate that treating attention as a feature map and applying convolution as a processing method significantly enhances Transformer performance. Chuanyang Zheng, Yihang Gao, Jiankai Sun, Jingyao Li 0001, Minbin Huang, Xiaozhe Ren, Michael Kwok-Po Ng, Zhenguo Li, Yu Li 0006 |
ACL (1) | 5 |
| 2025 | GRaD-Nav: Efficiently Learning Visual Drone Navigation with Gaussian Radiance Fields and Differentiable DynamicsabstractAutonomous visual navigation is an essential element in robot autonomy. Reinforcement learning (RL) offers a promising policy training paradigm. However, existing RL methods suffer from high sample complexity, poor sim-to-real transfer, and limited runtime adaptability. These problems are particularly challenging for drones, with complex nonlinear and unstable dynamics, and strong dynamic coupling between control and perception. In this paper, we propose a novel framework that integrates 3D Gaussian Splatting (3DGS) with differentiable deep reinforcement learning (DDRL) to train vision-based drone navigation policies. By leveraging high-fidelity 3D scene representations and differentiable simulation, our method improves sample efficiency and sim-to-real transfer. Additionally, we incorporate a Context-aided Estimator Network (CENet) to adapt to environmental variations at runtime. Moreover, by curriculum training in a mixture of different surrounding environments, we achieve in-task generalization, the ability to solve new instances of a task not seen during training. Drone hardware experiments demonstrate our method’s high training efficiency compared to state-of-the-art RL methods, zero shot sim-to-real transfer for real robot deployment without fine tuning, and ability to adapt to new instances within the same task class (e.g. to fly through a gate at different locations with different distractors in the environment). Our simulator and training framework are open-sourced at: https://github.com/Qianzhong-Chen/grad_nav. Qianzhong Chen, Jiankai Sun, Naixiang Gao, Timothy Chen, Mac Schwager |
IROS | 2 |
| 2025 | NopeRoomGS: Indoor 3D Gaussian Splatting Optimization without Camera Pose InputabstractRecent advances in 3D Gaussian Splatting (3DGS) have enabled real-time, high-fidelity view synthesis, but remain critically dependent on camera poses estimated by Structure-from-Motion (SfM), which is notoriously unreliable in textureless indoor environments. To eliminate this dependency, recent pose-free variants have been proposed, yet they often fail under abrupt camera motion due to unstable initialization and purely photometric objectives.
In this work, we introduce **Nope-RoomGS**, an optimization framework with no need for camera pose inputs, which effectively addresses the textureless regions and abrupt camera motion in indoor room environments through a local-to-global optimization paradigm for 3DGS reconstruction. In the local stage, we propose a lightweight local neural geometric representation to bootstrap a set of reliable local 3D Gaussians for separated short video clips, regularized by multi-frame tracking constraints and foundation model depth priors. This enables reliable initialization even in textureless regions or under abrupt camera motions.
In the global stage, we fuse local 3D Gaussians into a unified 3DGS representation through an alternating optimization strategy that jointly refines camera poses and Gaussian parameters, effectively mitigating gradient interference between them. Furthermore, we decompose camera pose optimization based on a piecewise planarity assumption, further enhancing robustness under abrupt camera motion. Extensive experiments on Replica, ScanNet and Tanks & Temples demonstrate the state-of-the-art performance of our method in both camera pose estimation and novel view synthesis. Mingde Yao, Fengjie Liang, Jiankai Sun, Menglu Wang 0003, Guofeng Zhang 0001, Linjiang Huang, Hongsheng Li 0001 |
NeurIPS | 5 |
| 2025 | Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long GenerationabstractRecent advances in language modeling have demonstrated the effectiveness of State Space Models (SSMs) for efficient sequence modeling. While hybrid architectures such as Samba and the decoder-decoder architecture, YOCO, have shown promising performance gains over Transformers, prior works have not investigated the efficiency potential of representation sharing between SSM layers. In this paper, we introduce the Gated Memory Unit (GMU), a simple yet effective mechanism for efficient memory sharing across layers. We apply it to create SambaY, a decoder-hybrid-decoder architecture that incorporates GMUs in the cross-decoder to share memory readout states from a Samba-based self-decoder. SambaY significantly enhances decoding efficiency, preserves linear pre-filling time complexity, and boosts long-context performance, all while eliminating the need for explicit positional encoding. Through extensive scaling experiments, we demonstrate that our model exhibits a significantly lower irreducible loss compared to a strong YOCO baseline, indicating superior performance scalability under large-scale compute regimes. Our largest model enhanced with Differential Attention, Phi4-mini-Flash-Reasoning, achieves significantly better performance than Phi4-mini-Reasoning on reasoning tasks such as Math500, AIME24/25, and GPQA Diamond without any reinforcement learning, while delivering up to 10× higher decoding throughput on 2K-length prompts with 32K generation length under the vLLM inference framework. We release our training codebase on open-source data at https://github.com/microsoft/ArchScale. Liliang Ren, Young Jin Kim 0006, Adam Atkinson, Zheng Zhan 0001, Jiankai Sun, Baolin Peng, Shuohang Wang, Hao Cheng 0002, Jianfeng Gao 0001, Weizhu Chen, Yelong Shen |
NeurIPS | 7 |
| 2025 | SAS: Simulated Attention ScoreabstractThe attention mechanism is a core component of the Transformer architecture.
Various methods have been developed to compute attention scores, including multi-head attention (MHA), multi-query attention, group-query attention and so on. We further analyze the MHA and observe that its performance improves as the number of attention heads increases, provided the hidden size per head remains sufficiently large. Therefore, increasing both the head count and hidden size per head with minimal parameter overhead can lead to significant performance gains at a low cost.
Motivated by this insight, we introduce Simulated Attention Score (SAS), which **maintains a compact model size while simulating a larger number of attention heads and hidden feature dimension per head.** This is achieved by projecting a low-dimensional head representation into a higher-dimensional space, effectively increasing attention capacity without increasing parameter count. Beyond the head representations, we further extend the simulation approach to feature dimension of the key and query embeddings, enhancing expressiveness by mimicking the behavior of a larger model while preserving the original model size.
**To control the parameter cost, we also propose Parameter-Efficient Attention Aggregation (PEAA).**
Comprehensive experiments on a variety of datasets and tasks demonstrate the effectiveness of the proposed SAS method, achieving significant improvements over different attention variants. Chuanyang Zheng, Jiankai Sun, Yihang Gao, Yuehao Wang, Peihao Wang, Liliang Ren, Hao Cheng 0002, Janardhan Kulkarni, Yelong Shen, Zhangyang Wang, Mac Schwager, Anderson Schneider, Jianfeng Gao 0001 |
NeurIPS | 2 |
| 2025 | Decoupled Entity Representation Learning for Pinterest Ads RankingabstractIn this paper, we introduce a novel framework following an upstreamdownstream paradigm to construct user and item (Pin) embeddings from diverse data sources, which are essential for Pinterest to deliver personalized Pins and ads effectively.Our upstream models are trained on extensive data sources featuring varied signals, utilizing complex architectures to capture intricate relationships between users and Pins on Pinterest.To ensure scalability of the upstream models, entity embeddings are learned, and regularly refreshed, rather than real-time computation, allowing for asynchronous interaction between the upstream and downstream models.These embeddings are then integrated as input features in numerous downstream tasks, including ad retrieval and ranking models for CTR and CVR predictions.We demonstrate that our framework achieves notable performance improvements in both offline and online settings across various downstream tasks.This framework Jie Liu 0092, Yinrui Li, Jiankai Sun, Kungang Li, Huasen Wu, Paulo Soares, Nan Li 0045, Haoyang Li 0013, Siping Ji, Ling Leng, Prathibha Deshikachar |
RecSys | 3 |
| 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment CollaborationabstractLarge, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin |
ICRA | 101 |
| 2024 | Privacy Preserving Conversion Modeling in Data Clean RoomabstractIn the realm of online advertising, accurately predicting the conversion rate (CVR) is crucial for enhancing advertising efficiency and user satisfaction. This paper addresses the challenge of CVR prediction while adhering to user privacy preferences and advertiser requirements. Traditional methods face obstacles such as the reluctance of advertisers to share sensitive conversion data and the limitations of model training in secure environments like data clean rooms. We propose a novel model training framework that enables collaborative model training without sharing sample-level gradients with the advertising platform. Our approach introduces several innovative components: (1) utilizing batch-level aggregated gradients instead of sample-level gradients to minimize privacy risks; (2) applying adapter-based parameter-efficient fine-tuning and gradient compression to reduce communication costs; and (3) employing de-biasing techniques to train the model under label differential privacy, thereby maintaining accuracy despite privacy-enhanced label perturbations. Our experimental results, conducted on industrial datasets, demonstrate that our method achieves competitive ROC-AUC performance while significantly decreasing communication overhead and complying with both advertisers’ privacy requirements and user privacy choices. This framework establishes a new standard for privacy-preserving, high-performance CVR prediction in the digital advertising landscape. Kungang Li, Xiangyi Chen, Ling Leng, Jiajing Xu 0003, Jiankai Sun, Behnam Rezaei |
RecSys | 5 |
| 2023 | DPAUC: Differentially Private AUC Computation in Federated LearningabstractFederated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on FL has mostly studied how to protect label privacy during model training. However, model evaluation in FL might also lead to the potential leakage of private label information. In this work, we propose an evaluation algorithm that can accurately compute the widely used AUC (area under the curve) metric when using the label differential privacy (DP) in FL. Through extensive experiments, we show our algorithms can compute accurate AUCs compared to the ground truth. The code is available at https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC Jiankai Sun, Xin Yang 0017, Yuanshun Yao, Junyuan Xie, Chong Wang 0002 |
AAAI | 1 |
| 2023 | LS-Net: COVID-19 Lesion Segmentation from CT Image via Diffusion Probabilistic Model
Aiwu Shi, Bei Sheng, Jiankai Sun, Gan Luo, Shuran Ma |
CGI (4) | 4 |
| 2023 | PSLF: Defending Against Label Leakage in Split LearningabstractWith increasing concern over data privacy, split learning has become a widely used distributed machine learning paradigm in practice, where two participants (namely the non-label party and the label party) own raw features and raw labels respectively, and jointly train a model. Although no raw data is communicated between the two parties during model training, several works have demonstrated that data privacy, especially label privacy, is still vulnerable in split learning, and have proposed several defense algorithms against label attacks. However, the theoretical guarantee on the privacy preservation of these algorithms is limited. In this work, we propose a novel Private Split Learning Framework (PSLF). In PSLF, the label party shares only the gradients computed by flipped labels with the non-label party, which improves privacy preservation on raw labels, and meanwhile, we further design an extra sub-model from true labels to improve prediction accuracy. We also design a Flipped Multi-Label Generation mechanism (FMLG) based on randomized response for the label party to generate flipped labels. FMLG is proven differentially private and the label party could make a trade-off between privacy and utility by setting the DP budget. In addition, we design an upsampling method to further protect the labels against some existing attacks. We have evaluated PSLF over real-world datasets to demonstrate its effectiveness in protecting label privacy and achieving promising prediction accuracy. Xinwei Wan, Jiankai Sun, Shengjie Wang 0001, Lei Chen 0096, Zhenzhe Zheng 0001, Fan Wu 0006, Guihai Chen |
CIKM | 2 |
| 2023 | Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive AttributesabstractEvaluating fairness can be challenging in practice because the sensitive attributes of data are often inaccessible due to privacy constraints. The go-to approach that the industry frequently adopts is using off-the-shelf proxy models to predict the missing sensitive attributes, e.g. Meta (Alao et al., 2021) and Twitter (Belli et al., 2022). Despite its popularity, there are three important questions unanswered: (1) Is directly using proxies efficacious in measuring fairness? (2) If not, is it possible to accurately evaluate fairness using proxies only? (3) Given the ethical controversy over infer-ring user private information, is it possible to only use weak (i.e. inaccurate) proxies in order to protect privacy? Our theoretical analyses show that directly using proxy models can give a false sense of (un)fairness. Second, we develop an algorithm that is able to measure fairness (provably) accurately with only three properly identified proxies. Third, we show that our algorithm allows the use of only weak proxies (e.g. with only 68.85% accuracy on COMPAS), adding an extra layer of protection on user privacy. Experiments validate our theoretical analyses and show our algorithm can effectively measure and mitigate bias. Our results imply a set of practical guidelines for prac-titioners on how to use proxies properly. Code is available at https://github.com/UCSC-REAL/fair-eval. Zhaowei Zhu, Yuanshun Yao, Jiankai Sun, Hang Li 0001, Yang Liu 0018 |
ICML | 3 |
| 2023 | Graph-Based Model-Agnostic Data Subsampling for Recommendation SystemsabstractData subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. Specifically, we study the topology of the user-item graph to estimate the importance of each user-item interaction (an edge in the user-item graph) via graph conductance, followed by a propagation step on the network to smooth out the estimated importance value. Since our proposed method is model-agnostic, we can marry the merits of both model-agnostic and model-based subsampling methods. Empirically, we show that combing the two consistently improves over any single method on the used datasets. Experimental results on KuaiRec and MIND datasets demonstrate that our proposed methods achieve superior results compared to baseline approaches. Jiankai Sun, Taiqing Wang, Ruocheng Guo, Liping Liu 0001, Aonan Zhang |
KDD | 2 |
| 2023 | Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics ModelabstractRobotic applications often involve working in environments that are uncertain, dynamic, and partially observable. Recently, diffusion models have been proposed for learning trajectory prediction models trained from expert demonstrations, which can be used for planning in robot tasks. Such models have demonstrated a strong ability to overcome challenges such as multi-modal action distributions, high-dimensional output spaces, and training instability. It is crucial to quantify the uncertainty of these dynamics models when using them for planning. In this paper, we quantify the uncertainty of diffusion dynamics models using Conformal Prediction (CP). Given a finite number of exchangeable expert trajectory examples (called the “calibration set”), we use CP to obtain a set in the trajectory space (called the “coverage region”) that is guaranteed to contain the output of the diffusion model with a user-defined probability (called the “coverage level”). In PlanCP, inspired by concepts from conformal prediction, we modify the loss function for training the diffusion model to include a quantile term to encourage more robust performance across the variety of training examples. At test time, we then calibrate PlanCP with a conformal prediction process to obtain coverage sets for the trajectory prediction with guaranteed coverage level. We evaluate our algorithm on various planning tasks and model-based offline reinforcement learning tasks and show that it reduces the uncertainty of the learned trajectory prediction model. As a by-product, our algorithm PlanCP outperforms prior algorithms on existing offline RL benchmarks and challenging continuous planning tasks. Our method can be combined with most model-based planning approaches to produce uncertainty estimates of the closed-loop system. Jiankai Sun, Yiqi Jiang, Jianing Qiu, Parth Nobel, Mykel J. Kochenderfer, Mac Schwager |
NeurIPS | 1 |
| 2023 | DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationabstractGraph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-item interactions as a bipartite graph, a GNN model generates user and item representations by aggregating embeddings of their neighbors. However, such an aggregation procedure often accumulates information purely based on the graph structure, overlooking the redundancy of the aggregated neighbors and resulting in poor diversity of the recommended list. In this paper, we propose diversifying GNN-based recommender systems by directly improving the embedding generation procedure. Particularly, we utilize the following three modules: submodular neighbor selection to find a subset of diverse neighbors to aggregate for each GNN node, layer attention to assign attention weights for each layer, and loss reweighting to focus on the learning of items belonging to long-tail categories. Blending the three modules into GNN, we present DGRec (Diversified GNN-based Recommender System) for diversified recommendation. Experiments on real-world datasets demonstrate that the proposed method can achieve the best diversity while keeping the accuracy comparable to state-of-the-art GNN-based recommender systems. We open source DGRec at https://github.com/YangLiangwei/DGRec. Liangwei Yang, Shengjie Wang 0001, Yunzhe Tao, Jiankai Sun, Xiaolong Liu 0012, Philip S. Yu, Taiqing Wang |
WSDM | 4 |
| 2023 | Lightweight bidirectional long short-term memory based on automated model pruning with application to bearing remaining useful life prediction
Jiankai Sun, Xin Zhang 0121 |
Eng. Appl. Artif. Intell. | 1 |
| 2023 | Large AI Models in Health Informatics: Applications, Challenges, and the FutureabstractLarge AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including: 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics. Jianing Qiu, Lin Li 0070, Jiankai Sun, Jiachuan Peng, Peilun Shi, Ruiyang Zhang, Yinzhao Dong, Kyle Lam, Frank P.-W. Lo, Bo Xiao 0002, Wu Yuan 0001, Ningli Wang, Dong Xu 0002, Benny P. L. Lo |
IEEE J. Biomed. Health Informatics | 3 |
| 2022 | Label Leakage and Protection in Two-party Split Learning
Oscar Li, Jiankai Sun, Xin Yang 0017, Weihao Gao, Junyuan Xie, Virginia Smith, Chong Wang 0002 |
ICLR | 2 |
| 2022 | Differentially private multi-party data release for linear regressionabstractDifferentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects. However the majority of prior work has focused on scenarios where a single party owns all the data. In this paper we focus on the multi-party setting, where different stakeholders own disjoint sets of attributes belonging to the same group of data subjects. Within the context of linear regression that allow all parties to train models on the complete data without the ability to infer private attributes or identities of individuals, we start with directly applying Gaussian mechanism and show it has the small eigenvalue problem. We further propose our novel method and prove it asymptotically converges to the optimal (non-private) solutions with increasing dataset size. We substantiate the theoretical results through experiments on both artificial and real-world datasets. Ruihan Wu, Xin Yang 0017, Yuanshun Yao, Jiankai Sun, Kilian Q. Weinberger, Chong Wang 0002 |
UAI | 4 |
| 2021 | HiABP: Hierarchical Initialized ABP for Unsupervised Representation LearningabstractAlthough Markov chain Monte Carlo (MCMC) is useful for generating samples from the posterior distribution, it often suffers from intractability when dealing with large-scale datasets. To address this issue, we propose Hierarchical Initialized Alternating Back-propagation (HiABP) for efficient Bayesian inference. Especially, we endow Alternating Backpropagation (ABP) method with a well-designed initializer and hierarchical structure, composing the pipeline of Initializing, Improving, and Learning back-propagation. It saves much time for the generative model to initialize the latent variable by constraining a sampler to be close to the true posterior distribution. The initialized latent variable is then improved significantly by an MCMC sampler. Thus the proposed method has the strengths of both methods, i.e., the effectiveness of MCMC and the efficiency of variational inference. Experimental results validate our framework can outperform other popular deep generative models in modeling natural images and learning from incomplete data. We further demonstrate the unsupervised disentanglement of hierarchical latent representation with controllable image synthesis. Jiankai Sun, Bolei Zhou |
AAAI | 1 |
| 2021 | Learning An End-to-End Structure for Retrieval in Large-Scale RecommendationsabstractOne of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorithm to find top candidates. In this paper, we present Deep Retrieval (DR), to learn a retrievable structure directly with user-item interaction data (e.g. clicks) without resorting to the Euclidean space assumption in ANN algorithms. DR's structure encodes all candidate items into a discrete latent space. Those latent codes for the candidates are model parameters and learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking. Empirically, we first demonstrate that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline on two public datasets. Moreover, we show that, in a live production recommendation system, a deployed DR approach significantly outperforms a well-tuned ANN baseline in terms of engagement metrics. To the best of our knowledge, DR is among the first non-ANN algorithms successfully deployed at the scale of hundreds of millions of items for industrial recommendation systems. Weihao Gao, Xiangjun Fan, Chong Wang 0002, Jiankai Sun, Wenzhi Xiao, Ruofan Ding, Xingyan Bin |
CIKM | 4 |
| 2020 | Transferable Active Grasping and Real Embodied DatasetabstractGrasping in cluttered scenes is challenging for robot vision systems, as detection accuracy can be hindered by partial occlusion of objects. We adopt a reinforcement learning (RL) framework and 3D vision architectures to search for feasible viewpoints for grasping by the use of hand-mounted RGB-D cameras. To overcome the disadvantages of photo-realistic environment simulation, we propose a large-scale dataset called Real Embodied Dataset (RED), which includes full-viewpoint real samples on the upper hemisphere with amodal annotation and enables a simulator that has real visual feedback. Based on this dataset, a practical 3-stage transferable active grasping pipeline is developed, that is adaptive to unseen clutter scenes. In our pipeline, we propose a novel mask-guided reward to overcome the sparse reward issue in grasping and ensure category-irrelevant behavior. The grasping pipeline and its possible variants are evaluated with extensive experiments both in simulation and on a real-world UR-5 robotic arm. Zelin Ye, Jiankai Sun, Yuda Fan, Fang Hu 0002, Chenxi Wang 0003, Cewu Lu |
ICRA | 3 |
| 2020 | SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloudabstract3D vehicle detection based on point cloud is a challenging task in real-world applications such as autonomous driving. Despite significant progress has been made, we observe two aspects to be further improved. First, the semantic context information in LiDAR is seldom explored in previous works, which may help identify ambiguous vehicles. Second, the distribution of point cloud on vehicles varies continuously with increasing depths, which may not be well modeled by a single model. In this work, we propose a unified model SegVoxelNet to address the above two problems. A semantic context encoder is proposed to leverage the free-of-charge semantic segmentation masks in the bird's eye view. Suspicious regions could be highlighted while noisy regions are suppressed by this module. To better deal with vehicles at different depths, a novel depth-aware head is designed to explicitly model the distribution differences and each part of the depth-aware head is made to focus on its own target detection range. Extensive experiments on the KITTI dataset show that the proposed method outperforms the state-of-the-art alternatives in both accuracy and efficiency with point cloud as input only. Hongwei Yi, Shaoshuai Shi, Mingyu Ding, Jiankai Sun, Kui Xu 0004, Hui Zhou 0005, Zhe Wang 0006, Sheng Li 0008 |
ICRA | 4 |
| 2020 | EndCold: An End-to-End Framework for Cold Question Routing in Community Question Answering ServicesabstractRouting newly posted questions (a.k.a cold questions) to potential answerers with suitable expertise in Community Question Answering sites (CQAs) is an important and challenging task. The existing methods either focus only on embedding the graph structural information and are less effective for newly posted questions, or adopt manually engineered feature vectors that are not as representative as the graph embedding methods. Therefore, we propose to address the challenge of leveraging heterogeneous graph and textual information for cold question routing by designing an end-to-end framework that jointly learns CQA node embeddings and finds best answerers for cold questions. We conducted extensive experiments to confirm the usefulness of incorporating the textual information from question tags and demonstrate that an end-2-end framework can achieve promising performances on routing newly posted questions asked by both existing users and newly registered users. Jiankai Sun, Jie Zhao 0013, Huan Sun 0001, Srinivasan Parthasarathy 0001 |
IJCAI | 1 |
| 2019 | Symmetrization for Embedding Directed GraphsabstractIn this paper, we propose to solve the directed graph embedding problem via a two stage approach: in the first stage, the graph is symmetrized in one of several possible ways, and in the second stage, the so-obtained symmetrized graph is embeded using any state-of-the-art (undirected) graph embedding algorithm. Note that it is not the objective of this paper to propose a new (undirected) graph embedding algorithm or discuss the strengths and weaknesses of existing ones; all we are saying is that whichever be the suitable graph embedding algorithm, it will fit in the above proposed symmetrization framework. Jiankai Sun, Srinivasan Parthasarathy 0001 |
AAAI | 1 |
| 2019 | ATP: Directed Graph Embedding with Asymmetric Transitivity PreservationabstractDirected graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e.g., question, answer, user. Asymmetric transitivity is an essential property of directed graphs, since it can play an important role in downstream graph inference and analysis. Question difficulty and user expertise follow the characteristic of asymmetric transitivity. Maintaining such properties, while reducing the graph to a lower dimensional vector embedding space, has been the focus of much recent research. In this paper, we tackle the challenge of directed graph embedding with asymmetric transitivity preservation and then leverage the proposed embedding method to solve a fundamental task in CQAs: how to appropriately route and assign newly posted questions to users with the suitable expertise and interest in CQAs. The technique incorporates graph hierarchy and reachability information naturally by relying on a nonlinear transformation that operates on the core reachability and implicit hierarchy within such graphs. Subsequently, the methodology levers a factorization-based approach to generate two embedding vectors for each node within the graph, to capture the asymmetric transitivity. Extensive experiments show that our framework consistently and significantly outperforms the state-of-the-art baselines on three diverse realworld tasks: link prediction, and question difficulty estimation and expert finding in online forums like Stack Exchange. Particularly, our framework can support inductive embedding learning for newly posted questions (unseen nodes during training), and therefore can properly route and assign these kinds of questions to experts in CQAs. Jiankai Sun, Bortik Bandyopadhyay, Armin Bashizade, Jiongqian Liang, P. Sadayappan, Srinivasan Parthasarathy 0001 |
AAAI | 1 |
| 2019 | CamNet: Coarse-to-Fine Retrieval for Camera Re-LocalizationabstractCamera re-localization is an important but challenging task in applications like robotics and autonomous driving. Recently, retrieval-based methods have been considered as a promising direction as they can be easily generalized to novel scenes. Despite significant progress has been made, we observe that the performance bottleneck of previous methods actually lies in the retrieval module. These methods use the same features for both retrieval and relative pose regression tasks which have potential conflicts in learning. To this end, here we present a coarse-to-fine retrieval-based deep learning framework, which includes three steps, i.e., image-based coarse retrieval, pose-based fine retrieval and precise relative pose regression. With our carefully designed retrieval module, the relative pose regression task can be surprisingly simpler. We design novel retrieval losses with batch hard sampling criterion and two-stage retrieval to locate samples that adapt to the relative pose regression task. Extensive experiments show that our model (CamNet) outperforms the state-of-the-art methods by a large margin on both indoor and outdoor datasets. Mingyu Ding, Zhe Wang 0006, Jiankai Sun, Jianping Shi, Ping Luo 0002 |
ICCV | 3 |
| 2018 | QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
Jiankai Sun, Sobhan Moosavi, Rajiv Ramnath, Srinivasan Parthasarathy 0001 |
ICWSM | 1 |
| 2018 | Semi-supervised Embedding in Attributed Networks with OutliersabstractIn this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over the state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO are interpretable as outlier scores and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting – flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task. Jiongqian Liang, Peter Jacobs, Jiankai Sun, Srinivasan Parthasarathy 0001 |
SDM | 3 |
| 2018 | ColdRoute: effective routing of cold questions in stack exchange sites
Jiankai Sun, Abhinav Vishnu, Aniket Chakrabarti, Charles Siegel, Srinivasan Parthasarathy 0001 |
Data Min. Knowl. Discov. | 1 |
| 2017 | Learning to Recommend Accurate and Diverse ItemsabstractIn this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as ground-truth for each user. We then represent each user or item as a vector resulted from the factorization of the user-item rating matrix. In our paper, we try to discover a factorization for matching the following supervised learning task. In doing this, we define two coupled optimization problems, parameterized matrix factorization and structural learning, to formulate our task. And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled problems. We also introduce a new pairwise accuracy metric and a normalized topic coverage diversity metric to measure the performance of accuracy and diversity respectively. Extensive experiments on benchmark datasets show the performance gains of DCF in comparison with the state-of-the-art algorithms. Peizhe Cheng, Shuaiqiang Wang, Jun Ma 0001, Jiankai Sun, Hui Xiong 0001 |
WWW | 4 |
| 2014 | VSRank: A Novel Framework for Ranking-Based Collaborative FilteringabstractCollaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating based and ranking based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and his or her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach. Shuaiqiang Wang, Jiankai Sun, Byron J. Gao, Jun Ma 0001 |
ACM Trans. Intell. Syst. Technol. | 2 |
| 2012 | Learning to rank for hybrid recommendationabstractMost existing recommender systems can be classified into two categories: collaborative filtering and content-based filtering. Hybrid recommender systems combine the advantages of the two for improved recommendation performance. Traditional recommender systems are rating-based. However, predicting ratings is an intermediate step towards their ultimate goal of generating rankings or recommendation lists. Learning to rank is an established means of predicting rankings and has recently demonstrated high promise in improving quality of recommendations. In this paper, we propose LRHR, the first attempt that adapts learning to rank to hybrid recommender systems. LRHR first defines novel representations for both users and items so that they can be content-comparable. Then, LRHR identifies a set of novel meta-level features for learning purposes. Finally, LRHR adopts RankSVM, a pairwise learning to rank algorithm, to generate recommendation lists of items for users. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms demonstrate the performance gain of our approach. Jiankai Sun, Shuaiqiang Wang, Byron J. Gao, Jun Ma 0001 |
CIKM | 1 |
| 2012 | Adapting vector space model to ranking-based collaborative filteringabstractCollaborative filtering (CF) is an effective technique addressing the information overload problem. Recently ranking-based CF methods have shown advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we seek accuracy improvement of ranking-based CF through adaptation of the vector space model, where we consider each user as a document and her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Then we use cosine similarity to select a neighborhood of users for the target user to make recommendations. Experiments on benchmarks in comparison with the state-of-the-art methods demonstrate the promise of our approach. Shuaiqiang Wang, Jiankai Sun, Byron J. Gao, Jun Ma 0001 |
CIKM | 2 |