Search Results for author: Jingtong Gao

Found 11 papers, 4 papers with code

SampleLLM: Optimizing Tabular Data Synthesis in Recommendations

no code implementations27 Jan 2025 Jingtong Gao, Zhaocheng Du, Xiaopeng Li, Yichao Wang, Xiangyang Li, Huifeng Guo, Ruiming Tang, Xiangyu Zhao

This limitation arises from their difficulty in capturing complex distributions and understanding feature relationships from sparse and limited data, along with their inability to grasp semantic feature relations.

Few-Shot Learning Recommendation Systems

Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark

1 code implementation23 Dec 2024 Xiaopeng Li, Jingtong Gao, Pengyue Jia, Yichao Wang, Wanyu Wang, Yejing Wang, Yuhao Wang, Xiangyu Zhao, Huifeng Guo, Ruiming Tang

Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention.

GAS: Generative Auto-bidding with Post-training Search

no code implementations22 Dec 2024 Yewen Li, Shuai Mao, Jingtong Gao, Nan Jiang, Yunjian Xu, Qingpeng Cai, Fei Pan, Peng Jiang, Bo An

We use weak-to-strong search alignment by training small critics for different preferences and an MCTS-inspired search to refine the model's output.

Computational Efficiency Sequential Decision Making

LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations

no code implementations18 Jun 2024 Jingtong Gao, Bo Chen, Weiwen Liu, Xiangyang Li, Yichao Wang, Wanyu Wang, Huifeng Guo, Ruiming Tang, Xiangyu Zhao

Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms.

Diversity Fairness +1

GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems

no code implementations6 Jun 2024 Sheng Zhang, Maolin Wang, Wanyu Wang, Jingtong Gao, Xiangyu Zhao, Yu Yang, Xuetao Wei, Zitao Liu, Tong Xu

Meanwhile, existing efficient SRS approaches struggle to embed high-quality semantic and positional information into latent representations.

Sequential Recommendation

BiVRec: Bidirectional View-based Multimodal Sequential Recommendation

no code implementations27 Feb 2024 Jiaxi Hu, Jingtong Gao, Xiangyu Zhao, Yuehong Hu, Yuxuan Liang, Yiqi Wang, Ming He, Zitao Liu, Hongzhi Yin

The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.

Semantic Similarity Semantic Textual Similarity +1

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

no code implementations5 Sep 2023 Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang

To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly.

Click-Through Rate Prediction

Multimodal Recommender Systems: A Survey

2 code implementations8 Feb 2023 Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang

In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.

Attribute Model Optimization +2

Multi-Task Recommendations with Reinforcement Learning

1 code implementation7 Feb 2023 Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai

To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.

Multi-Task Learning Recommendation Systems +3

A Comprehensive Survey on Trustworthy Recommender Systems

no code implementations21 Sep 2022 Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li

As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.

Fairness Recommendation Systems +1

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