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Yandex study compares GNN vs end-to-end item embeddings for recommendation systems

By PulseAugur Editorial · [1 sources] ·

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 →

Yandex study compares GNN vs end-to-end item embeddings for recommendation systems

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Academic paper detailing a comparative study of embedding strategies for recommendation systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Kirill Khrylchenko ·

    Embedding Items at Scale: Comparing GNN-Based and ID-Based Item Embeddings in the Yandex Ecosystem

    Transformer-based sequential recommendation models, which process sequences of user-item interactions, rely heavily on the item embedding strategy. Existing approaches either use pretrained item embeddings or learn them end-to-end with the transformer. To the best of our knowledg…

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