mlsanityData to 7 Oct 2026

Building a Scalable, Effective, and Steerable Search and Ranking Platform

arXiv 2409.02856 ↗First posted 4 Sep 2024cs.IR cs.LGcited by 3

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Abstract (arXiv)

Modern e-commerce platforms offer vast product selections, making it difficult for customers to find items that they like and that are relevant to their current session intent. This is why it is key for e-commerce platforms to have near real-time scalable and adaptable personalized ranking and search systems. While numerous methods exist in the scientific literature for building such systems, many are unsuitable for large-scale industrial use due to complexity and performance limitations. Consequently, industrial ranking systems often resort to computationally efficient yet simplistic retrieval or candidate generation approaches, which overlook near real-time and heterogeneous customer signals, which results in a less personalized and relevant experience. Moreover, related customer experiences are served by completely different systems, which increases complexity, maintenance, and inconsistent experiences. In this paper, we present a personalized, adaptable near real-time ranking platform that is reusable across various use cases, such as browsing and search, and that is able to cater to millions of items and customers under heavy load (thousands of requests per second). We employ transformer-based models through different ranking layers which can learn complex behavior patterns directly from customer action sequences while being able to incorporate temporal (e.g. in-session) and contextual information. We validate our system through a series of comprehensive offline and online real-world experiments at a large online e-commerce platform, and we demonstrate its superiority when compared to existing systems, both in terms of customer experience as well as in net revenue. Finally, we share the lessons learned from building a comprehensive, modern ranking platform for use in a large-scale e-commerce environment.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 4 Sep 2024.

Builds on

11 of 33 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Predictioncited by 176
  3. 2019Behavior Sequence Transformer for E-commerce Recommendation in Alibabacited by 128
  4. 2022PinnerFormer: Sequence Modeling for User Representation at Pinterestcited by 57
  5. 2023TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterestcited by 35
  6. 2023gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Samplingcited by 29
  7. 2023Widespread Flaws in Offline Evaluation of Recommender Systemscited by 21
  8. 2022Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?cited by 17
  9. 2016Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mappingcited by 10
  10. 2023Reusable Self-Attention Recommender Systems in Fashion Industry Applicationscited by 3
  11. 2022Reusable Self-Attention-based Recommender System for Fashioncited by 2

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Efficient and Effective Query Context-Aware Learning-to-Rank Model for Sequential Recommendationcited by 2
  2. 2025DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendationcited by 2
  3. 2025Short-Form Video Recommendations with Multimodal Embeddings: Addressing Cold-Start and Bias Challengescited by 0