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.