Interactive Recommendation Agent with Active User Commands
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 capture users' nuanced behavior motivations and
intentions. In turn, current systems cannot also distinguish which specific
item attributes drive user satisfaction or dissatisfaction, resulting in
inaccurate preference modeling. These fundamental limitations create a
persistent gap between user intentions and system interpretations, ultimately
undermining user satisfaction and harming system effectiveness.
  To address these limitations, we introduce the Interactive Recommendation
Feed (IRF), a pioneering paradigm that enables natural language commands within
mainstream recommendation feeds. Unlike traditional systems that confine users
to passive implicit behavioral influence, IRF empowers active explicit control
over recommendation policies through real-time linguistic commands. To support
this paradigm, we develop RecBot, a dual-agent architecture where a Parser
Agent transforms linguistic expressions into structured preferences and a
Planner Agent dynamically orchestrates adaptive tool chains for on-the-fly
policy adjustment. To enable practical deployment, we employ
simulation-augmented knowledge distillation to achieve efficient performance
while maintaining strong reasoning capabilities. Through extensive offline and
long-term online experiments, RecBot shows significant improvements in both
user satisfaction and business outcomes.
68d5ee2e8ccd91bdd39ffce3
Jiakai Tang
claimed_verified
2025-09-26T12:20:27.703Z
65acfb3a14e6582c30b4ce76
https://cdn-avatars.huggingface.co/v1/production/uploads/65acfb3a14e6582c30b4ce76/RhEhePggBtyM0RIIqXQen.jpeg
TangJiakai
user
TangJiakai5704
68d5ee2e8ccd91bdd39ffce4
Yujie Luo
68d5ee2e8ccd91bdd39ffce5
Xunke Xi
68d5ee2e8ccd91bdd39ffce6
Fei Sun
68d5ee2e8ccd91bdd39ffce7
Xueyang Feng
68d5ee2e8ccd91bdd39ffce8
Sunhao Dai
68d5ee2e8ccd91bdd39ffce9
Chao Yi
68d5ee2e8ccd91bdd39ffcea
Dian Chen
68d5ee2e8ccd91bdd39ffceb
Zhujin Gao
68d5ee2e8ccd91bdd39ffcec
Yang Li
68d5ee2e8ccd91bdd39ffced
Xu Chen
68d5ee2e8ccd91bdd39ffcee
Wen Chen
68d5ee2e8ccd91bdd39ffcef
Jian Wu
68d5ee2e8ccd91bdd39ffcf0
Yuning Jiang
68d5ee2e8ccd91bdd39ffcf1
Bo Zheng
Interactive Recommendation Agent with Active User Commands
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 capture users' nuanced behavior motivations and
intentions. In turn, current systems cannot also distinguish which specific
item attributes drive user satisfaction or dissatisfaction, resulting in
inaccurate preference modeling. These fundamental limitations create a
persistent gap between user intentions and system interpretations, ultimately
undermining user satisfaction and harming system effectiveness.
  To address these limitations, we introduce the Interactive Recommendation
Feed (IRF), a pioneering paradigm that enables natural language commands within
mainstream recommendation feeds. Unlike traditional systems that confine users
to passive implicit behavioral influence, IRF empowers active explicit control
over recommendation policies through real-time linguistic commands. To support
this paradigm, we develop RecBot, a dual-agent architecture where a Parser
Agent transforms linguistic expressions into structured preferences and a
Planner Agent dynamically orchestrates adaptive tool chains for on-the-fly
policy adjustment. To enable practical deployment, we employ
simulation-augmented knowledge distillation to achieve efficient performance
while maintaining strong reasoning capabilities. Through extensive offline and
long-term online experiments, RecBot shows significant improvements in both
user satisfaction and business outcomes.
IRF, a new recommendation system using natural language commands, improves user satisfaction and business outcomes through a dual-agent architecture and simulation-augmented knowledge distillation.
Interactive Recommendation Feed IRF Parser Agent Planner Agent dual-agent architecture simulation-augmented knowledge distillation