Recommendation Systems (RecSys)
Our research in Recommendation Systems (RecSys) is empowering businesses to deliver smarter, more personalized customer experiences. By harnessing data-driven insights, we are developing AI solutions that enhance decision-making, optimize engagement, and unlock new growth opportunities across diverse industries.
Our Research on RecSys
Check Out Our Latest Research
Bidding Optimization for DSP
Bidding Optimization for DSP is a project focused on developing a fast and efficient bidding system for demand-side platforms (DSP). Using Machine Learning, the system maximizes KPIs and optimizes ad purchases within a 10ms latency. Key achievements include improving ROAS across major campaigns, handling millions of bids per second, and optimizing costs. The project also explored Dynamic Creative Optimization (DCO) and expanded to global SSPs.
Promotion Optimization Project
Promotion Optimization Project focuses on using AI to enhance promotion planning and operations. By developing a sales prediction model, the project automates event planning to maximize revenue while ensuring the target profit is exceeded. It suggests the optimal products, discount rates, and event strategies to achieve the highest sales. The AI-driven events have consistently maintained an average revenue increase of 104% compared to traditional event methods.
AlphaDom Project
In the AlphaDom Project, we partnered with Cociety to develop cutting-edge AI solutions for the 'Worker Shop' wine store. The project featured personalized wine recommendations powered by AI, customized receipt sketches, and auto-generated messaging, showcasing the versatility and innovation of retail media technology. This project serves as a key milestone in creating an Omni-channel Retail Media Testbed, effectively blending online and offline experiences, and demonstrating significant potential for scaling in the retail sector.
