Search Results for author: Bowei He

Found 14 papers, 8 papers with code

Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation

no code implementations25 Jan 2024 Sichun Luo, Yuxuan Yao, Bowei He, Yinya Huang, Aojun Zhou, Xinyi Zhang, Yuanzhang Xiao, Mingjie Zhan, Linqi Song

Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior.

Data Augmentation

Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks

no code implementations12 Jan 2024 Ziqiang Cui, Xing Tang, Yang Qiao, Bowei He, Liang Chen, Xiuqiang He, Chen Ma

Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings.

Representation Learning

RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation

no code implementations26 Dec 2023 Sichun Luo, Bowei He, Haohan Zhao, Yinya Huang, Aojun Zhou, Zongpeng Li, Yuanzhang Xiao, Mingjie Zhan, Linqi Song

In this paper, we introduce RecRanker, tailored for instruction tuning LLM to serve as the \textbf{Ranker} for top-\textit{k} \textbf{Rec}ommendations.

In-Context Learning Language Modelling +3

Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization

no code implementations7 Oct 2023 Zexu Sun, Bowei He, Ming Ma, Jiakai Tang, Yuchen Wang, Chen Ma, Dugang Liu

Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features.

feature selection Marketing

Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks

1 code implementation24 Aug 2023 Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Jiaming Liu, Hao Dong, Peng Gao

However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes.

3D Semantic Segmentation Few-shot 3D semantic segmentation +1

Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System

no code implementations15 Aug 2023 Bowei He, Xu He, Renrui Zhang, Yingxue Zhang, Ruiming Tang, Chen Ma

The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems.

Recommendation Systems

Mutually Guided Few-shot Learning for Relational Triple Extraction

1 code implementation23 Jun 2023 Chengmei Yang, Shuai Jiang, Bowei He, Chen Ma, Lianghua He

Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations.

Cross-Domain Few-Shot Knowledge Graphs +2

Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems

1 code implementation3 May 2023 Xiong-Hui Chen, Bowei He, Yang Yu, Qingyang Li, Zhiwei Qin, Wenjie Shang, Jieping Ye, Chen Ma

However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.

Decision Making Recommendation Systems +1

Dynamically Expandable Graph Convolution for Streaming Recommendation

1 code implementation21 Mar 2023 Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma

Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.

Graph Learning Recommendation Systems

Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning

no code implementations7 Feb 2023 Rundong Wang, Longtao Zheng, Wei Qiu, Bowei He, Bo An, Zinovi Rabinovich, Yujing Hu, Yingfeng Chen, Tangjie Lv, Changjie Fan

Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.

Multi-agent Reinforcement Learning reinforcement-learning +1

Result Diversification in Search and Recommendation: A Survey

1 code implementation29 Dec 2022 Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Bowei He, Bhaskar Mitra, Xue Liu

Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.

Retrieval

PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning

2 code implementations ICCV 2023 Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Ziyao Zeng, Zipeng Qin, Shanghang Zhang, Peng Gao

In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.

Ranked #2 on Training-free 3D Part Segmentation on ShapeNet-Part (using extra training data)

3D Classification 3D Object Detection +9

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