Learning What to Recommend and Cache: Cost-Guided Multi-Agent Control at the Edge

Recommendation-enabled edge caching does more than anticipate demand: it helps create the demand that the network must then serve. This coupling is powerful yet brittle, because recommendation response varies across users while every induced request must respect finite cache capacity and heterogeneous delivery routes. We cast the interaction as a sequential multi-agent control problem and introduce PA-MADDPG to connect behavior-conditioned demand with feasible edge decisions. Six descriptors distill each rating history into a behavioral profile, from which a Broad Learning System infers a Big Five representation. A cross-fitted response model blends this representation with list context, estimating how strongly recommendations may redirect individual demand without revealing the responses reserved for evaluation. The user model informs decentralized actors and centralized critics, while a cost-guided projection combines learned scores with marginal delivery savings to produce capacity-feasible caching and top-K recommendation decisions. Together, these elements align the demand that recommendations create with the resources available to serve it. On the synthesized Personality-INFOCOM setting, PA-MADDPG lowers cost across ten seeds against five adaptations, albeit with higher latency; ablations support projection but not a separable personality effect.