Researchers at Yandex have conducted a large-scale study comparing two methods for generating item embeddings in transformer-based recommendation systems: pretrained graph neural network (GNN) embeddings and end-to-end trainable embeddings. Their findings indicate that while pretraining offers benefits for smaller datasets, it provides no significant advantage for large-scale models trained on extensive data. The study utilized production systems from Yandex Market and Yandex Music, and also included a low-resource dataset from Yandex Lavka. AI
IMPACT This research provides insights into optimizing recommendation system performance by comparing different embedding techniques, potentially impacting how large-scale systems are trained.
RANK_REASON Academic paper detailing a comparative study of embedding strategies for recommendation systems. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.IR (Information Retrieval) →
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
