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[2602.16124] Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System
Summary
The paper presents a novel approach called MultiFaceted Learnable Index (MFLI) for enhancing ANN-based retrieval in large-scale recommendation systems, addressing key limitations of traditional methods.
Why It Matters
This research is significant as it proposes a unified framework that improves retrieval efficiency and accuracy in recommendation systems, which are crucial for user engagement and content delivery in various applications. The advancements could lead to better user experiences and lower operational costs.
Key Takeaways
- MFLI integrates item embeddings and indices into a single learning framework.
- The approach eliminates the need for ANN search during serving, reducing computational costs.
- Experiments show significant improvements in recall, cold-content delivery, and semantic relevance.
- Real-world deployment of MFLI resulted in enhanced user engagement and reduced popularity bias.
- The method supports real-time updates, making it adaptable to dynamic content environments.
Computer Science > Information Retrieval arXiv:2602.16124 (cs) [Submitted on 18 Feb 2026] Title:Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System Authors:Jiang Zhang, Yubo Wang, Wei Chang, Lu Han, Xingying Cheng, Feng Zhang, Min Li, Songhao Jiang, Wei Zheng, Harry Tran, Zhen Wang, Lei Chen, Yueming Wang, Benyu Zhang, Xiangjun Fan, Bi Xue, Qifan Wang View a PDF of the paper titled Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System, by Jiang Zhang and 16 other authors View PDF HTML (experimental) Abstract:Approximate nearest neighbor (ANN) search is widely used in the retrieval stage of large-scale recommendation systems. In this stage, candidate items are indexed using their learned embedding vectors, and ANN search is executed for each user (or item) query to retrieve a set of relevant items. However, ANN-based retrieval has two key limitations. First, item embeddings and their indices are typically learned in separate stages: indexing is often performed offline after embeddings are trained, which can yield suboptimal retrieval quality-especially for newly created items. Second, although ANN offers sublinear query time, it must still be run for every request, incurring substantial computation cost at industry scale. In this paper, we propose MultiFaceted Learnable Index (MFLI), a scalable, real-time retrieval paradigm that learns multifaceted item embeddings and indices within ...
