1.6k citations · 1.6k across the 17 of their papers we have counts for
9 papers · 1 filter
RecGPT-V2 Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +32
Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. Whil…
The 2nd Workshop on Human-Centered Recommender Systems
Kaike Zhang, Jiakai Tang, Du Su +6
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and…
Interactive Recommendation Agent with Active User Commands
Jiakai Tang, Yujie Luo, Xunke Xi +12
Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to captu…
OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Sunhao Dai, Jiakai Tang, Jiahua Wu +13
Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain…
RecGPT Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +51
Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, mo…
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent
Xueyang Feng, Jingsen Zhang, Jiakai Tang +6
Recent advancements in Large Language Models (LLMs) have significantly propelled the development of Conversational Recommendation Agents (CRAs). However, these agents often generat…
Explainable Recommendation with Simulated Human Feedback
Jiakai Tang, Jingsen Zhang, Zihang Tian +3
Recent advancements in explainable recommendation have greatly bolstered user experience by elucidating the decision-making rationale. However, the existing methods actually fail t…
Improving Retrospective Language Agents via Joint Policy Gradient Optimization
Xueyang Feng, Bo Lan, Quanyu Dai +5
In recent research advancements within the community, large language models (LLMs) have sparked great interest in creating autonomous agents. However, current prompt-based agents o…
Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation
Jiakai Tang, Sunhao Dai, Teng Shi +5
Sequential Recommendation (SeqRec) aims to predict the next item by capturing sequential patterns from users' historical interactions, playing a crucial role in many real-world rec…