41 citations · 122 across the 27 of their papers we have counts for
27 papers
Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation
Long Zhang, Hao Jiang, Sheng Yu +3
While large language models (LLMs) have advanced ID-based recommendation through Semantic ID (SID) modeling, existing SID generation frameworks largely follow a single-representati…
Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
Yuecheng Li, Zeyu Song, Jing Yao +3
Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry. However, aligning the LLM's semantic space with the recommender's ID spac…
Reinforced Preference Optimization for Reasoning-Augmented Recommendations
Jingtong Gao, Zeyu Song, Chi Lu +7
Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhanc…
RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment
Yuecheng Li, Hengwei Ju, Zeyu Song +4
Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains largely overlooked: the semantic hetero…
R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
Hao Gu, Rui Zhong, Yu Xia +4
Harnessing Large Language Models (LLMs) for recommendation systems has emerged as a prominent avenue, drawing substantial research interest. However, existing approaches primarily…
Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model
Yu Xia, Rui Zhong, Hao Gu +4
Large Language Models (LLMs) have garnered significant attention in Recommendation Systems (RS) due to their extensive world knowledge and robust reasoning capabilities. However, a…
Generative Auto-Bidding with Value-Guided Explorations
Jingtong Gao, Yewen Li, Shuai Mao +8
Auto-bidding, with its strong capability to optimize bidding decisions within dynamic and competitive online environments, has become a pivotal strategy for advertising platforms.…
CHIME: A Compressive Framework for Holistic Interest Modeling
Yong Bai, Rui Xiang, Kaiyuan Li +5
Modeling holistic user interests is important for improving recommendation systems but is challenged by high computational cost and difficulty in handling diverse information with…
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation
Kaiyuan Li, Rui Xiang, Yong Bai +5
Multi-modal sequential recommendation systems leverage auxiliary signals (e.g., text, images) to alleviate data sparsity in user-item interactions. While recent methods exploit lar…
Learning Cascade Ranking as One Network
Yunli Wang, Zhen Zhang, Zhiqiang Wang +6
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage…
From Principles to Applications: A Comprehensive Survey of Discrete Tokenizers in Generation, Comprehension, Recommendation, and Information Retrieval
Jian Jia, Jingtong Gao, Ben Xue +6
Discrete tokenizers have emerged as indispensable components in modern machine learning systems, particularly within the context of autoregressive modeling and large language model…
Adaptive: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling
Wenxuan Sun, Zixuan Yang, Yunli Wang +8
Advertising systems often face the multi-domain challenge, where data distributions vary significantly across scenarios. Existing domain adaptation methods primarily focus on build…
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy
Te Yang, Jian Jia, Xiangyu Zhu +9
Large Language Models (LLMs) have strong instruction-following capability to interpret and execute tasks as directed by human commands. Multimodal Large Language Models (MLLMs) hav…
Scaling Laws for Online Advertisement Retrieval
Yunli Wang, Zhen Zhang, Zixuan Yang +9
The scaling law is a notable property of neural network models and has significantly propelled the development of large language models. Scaling laws hold great promise in guiding…
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
Zijian Zhang, Shuchang Liu, Jiaao Yu +9
Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehen…
LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
Jian Jia, Yipei Wang, Yan Li +8
Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic…
RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm
Yabin Zhang, Wenhui Yu, Erhan Zhang +4
ChatGPT has achieved remarkable success in natural language understanding. Considering that recommendation is indeed a conversation between users and the system with items as words…
KuaiSim: A Comprehensive Simulator for Recommender Systems
Kesen Zhao, Shuchang Liu, Qingpeng Cai +5
Reinforcement Learning (RL)-based recommender systems (RSs) have garnered considerable attention due to their ability to learn optimal recommendation policies and maximize long-ter…
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems
Zhenghai Xue, Qingpeng Cai, Bin Yang +4
The field of Reinforcement Learning (RL) has garnered increasing attention for its ability of optimizing user retention in recommender systems. A primary obstacle in this optimizat…
A Large Language Model Enhanced Conversational Recommender System
Yue Feng, Shuchang Liu, Zhenghai Xue +5
Conversational recommender systems (CRSs) aim to recommend high-quality items to users through a dialogue interface. It usually contains multiple sub-tasks, such as user preference…
Generative Flow Network for Listwise Recommendation
Shuchang Liu, Qingpeng Cai, Zhankui He +5
Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches th…
State Regularized Policy Optimization on Data with Dynamics Shift
Zhenghai Xue, Qingpeng Cai, Shuchang Liu +4
In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of cu…
Exploration and Regularization of the Latent Action Space in Recommendation
Shuchang Liu, Qingpeng Cai, Bowen Sun +7
In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction.…
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Yuan Zhang, Xue Dong, Weijie Ding +3
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterat…