Event Matchmaking and Feedback Analytics

Improved matchmaking search and LLM-based feedback analysis for a Singapore event-tech company.

+40%
Increase in search relevance with collaborative filtering
-25%
Reduction in server response time after PostgreSQL tuning
96%
Sentiment classification accuracy, evaluated on a 200-sample labelled set

Role: Backend Engineer (freelance, remote)
Period: Sep 2023 to Dec 2023
Client: Jublia
Project type: Project
Room in the 3D HQ: L3:event-tech-search-backend

Results

+40%
Increase in search relevance with collaborative filtering
The measurement method was not recorded.
-25%
Reduction in server response time after PostgreSQL tuning
96%
Sentiment classification accuracy, evaluated on a 200-sample labelled set
Based on a small evaluation set.
+50%
Increase in feedback analysis speed with queued daily reports

Problem

Jublia's trade-event matchmaking needed more relevant results for people and exhibitors. Manual analysis of post-event NPS, CSAT and free-text feedback was too slow.

Contribution

I improved match ranking with Elasticsearch and collaborative filtering.
I tuned PostgreSQL queries serving the slowest endpoints.
I built a LangChain and OpenAI sentiment service for NPS and CSAT feedback and evaluated it on a 200-sample labelled set.
I queued daily sentiment reports with RabbitMQ, removed legacy Flask features and ran Locust load tests.