The motivation behind this thesis arises from the high labor costs commonly
observed in Canadian communities, where residents are often forced to acquire multiple skills to cope with everyday needs. A web-based skill-exchange platform—where
two people trade services using their respective skills—would be valuable. However,
population sparsity often makes matching difficult. To address this challenge, we explore a novel approach to the sharing economy: a local mutual-aid platform. Within
this platform, users can consume services provided by others through virtual tokens,
while the only way to earn tokens is by offering services themselves. Since these tokens are purely virtual, mutual-aid activities do not incur legal liabilities, nor do
they risk creating full-time workers motivated solely by financial profit, which could
undermine the spirit of reciprocity.
On the implementation side, this thesis leverages an optimized Retrieval-Augmented
Generation (RAG) approach to enable query handling under sparse data conditions,
ensuring that even limited datasets can yield accurate and explainable recommendations. Furthermore, a self-developed distributed transaction manager based on
the Saga pattern ensures the integrity of user data across distributed environments,
supporting consistent balance updates, order confirmations, and notifications.
The prototype platform we developed demonstrates how combining modern AI
techniques with lightweight distributed systems can provide both practical utility
and long-term sustainability for local help ecosystems.
