Recommendation algorithms improve by coordinating design choices and experiments
RecRSI: Mechanism-Guided Discovery of Recommendation Algorithms
Information RetrievalArtificial Intelligence
Summary
Recommendation algorithms help decide what content or products to show you online, but making them better is tricky because many design choices interact with each other. The authors created a framework called RecRSI that uses clear ideas about how changes to recommendation designs should work and then tests those ideas step-by-step. This approach helps the algorithms improve by learning which changes actually work well together. They tested their method on several algorithms and data sets and found it consistently improved recommendation quality.
What this means in practice
- •For web recommendation engineers: Improve online recommendation quality by using guided experiments to coordinate design changes effectively.
- •For e-commerce platform developers: Enhance product suggestions by iteratively testing and refining recommendation components informed by prior experimental outcomes.
Authors
Tingran Gan, Zhouziyue Lu, Dian Jin, Tao Li, Yang Gao, Ming Liu, Weiwen Liu, Jianghao Lin, Weinan Zhang, Yong Yu
Abstract
Improving recommendation algorithms for the Web requires coordinating interacting design choices and learning from experiments. We present RecRSI, an LLM-based research framework that uses mechanism hypotheses to organize algorithm discovery. Each hypothesis links a proposed mechanism change to a reference design, a rationale, and an expected observation. MechSpace derives implementable primitives and composition rules from the task source; Research Line uses this space and prior evidence to develop and test hypotheses; Mechanism Feedback carries outcomes and their design context into subsequent research. This organization makes each experiment inform which mechanisms to develop, revise, or combine next. Across four initial recommenders and four datasets, RecRSI yields average relative improvements of 8.48% in NDCG@5 and 6.88% in HR@5 over the initial algorithms, and 2.60% and 2.20%, respectively, over the strongest competing result for each setting and metric. Component and process studies link these gains to coordinated mechanism choices and evidence-guided follow-up. Our code is publicly available at https://github.com/DynamisG210/RecRSI.