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 a unified framework and eliminates ANN search at serving time. Specifically, we construct a multifaceted hierarchical codebook via residual quantization of item embeddings and co-train the codebook with the embeddings. We further introduce an efficient multifaceted indexing structure and mechanisms that support real-time updates. At serving time, the learned hierarchical indices are used directly to identify relevant items, avoiding ANN search altogether. Extensive experiments on real-world data with billions of users show that MFLI improves recall on engagement tasks by up to 11.8%, cold-content delivery by up to 57.29%, and semantic relevance by 13.5% compared with prior state-of-the-art methods. We also deploy MFLI in the system and report online experimental results demonstrating improved engagement, less popularity bias, and higher serving efficiency.
Figures & tables
Figure 1. Traditional ANN-based retrieval contains three stages: item embedding learning, offline item indexing, and online ANN search at serving time. In contrast, LI-based retrieval jointly learns item embeddings and indices during training, and performs fast index lookup during serving.
Figure 2. Overview of MFLI framework. During training phase, it learns a multifaceted embedding for each item, and conducts residual quantization on candidate item embeddings by facet in parallel. The codebook and item embeddings are jointly trained via a multifacted loss and a quantized multifaceted loss. The serving part consists of four modules: 1) index lookup, which looks up the multifaceted indices of query item ids; 2) index selection module that selects top K indices; 3) item lookup, which fetches all items in each selected index; 4) per-index rerank, which selects top N items for each selected index.
Figure 3. Illustration of index rebalancing, where undersized indices are merged, oversized indices are split, and mask items are assigned with an invalid index.
Figure 4. Delta update of MFLI’s indexing structure. Every Δt time (e.g., 1 min), a delta snapshot storing the indexing structure of fresh items is produced. Every ΔT time (e.g., 30 minutes), a full snapshot storing the refreshed indexing structure of the full item pool is produced. MFLI fetches items from both the full and delta snapshots during serving.
Baseline
Recall
Semantic Relevance
VVC
Like
LWT
CCD
T1 topic
T2 topic
NeuCF ( He et al., 2017 )
21.51%
24.44%
25.85%
7.54%
24.80%
17.10%
MoL ( Zhai et al., 2023 )
22.67% (+5.39%)
25.78% (+5.48%)
26.84% (+3.83%)
7.60% (+0.80%)
39.65% (+59.88%)
26.84% (+56.96%)
HLLM ( Chen et al., 2024 )
22.39% (+4.09%)
25.28% (+3.44%)
26.61% (+2.94%)
7.97% (+5.70%)
38.94% (+57.02%)
26.48% (+54.85%)
HSTU ( Zhai et al., 2024 )
23.64% (+9.90%)
28.07% (+14.85%)
26.46% (+2.36%)
12.08% (+60.21%)
28.00% (+12.90%)
19.07% (+11.52%)
MTMH ( Zhang et al., 2025 )
22.85% (+6.23%)
26.11% (+6.83%)
27.23% (+5.34%)
8.18% (+8.49%)
46.10% (+85.89%)
33.55% (+96.20%)
Table 1. Performance comparison of MFLI and baseline methods. For recall, we report recall@1000 on VVC (Video View Complete), Like, LWT (Long Watch Time), and CCD (Cold Content Delivery) tasks. For semantic relevance, we report the average topic match rate between the trigger and retrieved items at the T1 (coarse-grained) and T2 (fine-grained) levels.
Baseline
Recall
Semantic Relevance
VVC
Like
LWT
CCD
T1 topic
T2 topic
MFLI
24.04%
26.72%
27.49%
11.86%
52.31%
38.70%
- Split-and-merge
19.60% (-18.47%)
23.05% (-13.74%)
22.65% (-17.61%)
11.32% (-4.55%)
49.35% (-5.66%)
34.49% (-10.88%)
- Index selection
21.61% (-10.11%)
25.15% (-5.88%)
25.30% (-7.97%)
12.50% (+5.40%)
53.39% (+2.06%)
40.17% (+3.80%)
- Delayed start
23.60% (-1.83%)
26.38% (-1.27%)
27.64% (+0.55%)
12.24% (+3.20%)
51.70% (-1.17%)
38.28% (-1.09%)
Table 2. Ablation study results of MFLI. We independently remove split-and-merge index rebalancing, top-k index selection, and delayed start to quantify the impact of each component on MFLI’s recall and semantic relevance. VVC, Like, LWT, and CCD denote the video view complete, like, long watch time, and cold content delivery tasks, respectively.
Figure 5. Analysis of learnt indices in MFLI. (a) Index-size distribution (items per index). (b) Item distribution across index-size buckets (items per index-size buckets). (c) Index usage (percentage of non-empty indices) over training snapshots. (d) Intra-index vs. inter-index relevance.
Table 3. Effects of number of facets and codebook size.
Category
Metric
Relative change
Video view
[0, 5k)/[5k, 50k)
+279%/+54%
[50k, 100k)/[100k, ∞ )
+11%/-2%
Freshness
[0, 6h)
+221%
[0, 24h)
+10%
Engagement
Explicit signals
+0.08%
Diversity
+0.30%
Table 4. Aggregated 7-day online A/B testing results. MFLI reduces popularity bias by shifting exposure toward low-VV items and improves freshness, engagement efficiency (more explicit and diverse engagement ), and serving throughput.
Num. of facets
Recall
Semantic relevance
Throughput
VVC
Like
LWT
CCD
I1 topic
I2 topic
One
20.72% (-13.81%)
24.19% (-9.47%)
24.49% (-10.91%)
10.53% (-11.21%)
45.52% (-12.98%)
31.51% (-18.58%)
+0.0%
Two (default)
24.04%
26.72%
27.49%
11.86%
52.31%
38.70%
-18.8%
Three
24.13% (+0.37%)
27.53% (+3.03%)
28.19% (+2.55%)
12.23% (+3.12%)
50.60% (-3.27%)
37.11% (-4.11%)
-23.9%
Table 5. Effects of number of facets on MFLI’s performance. Note that VVC, Like, LWT, CCD represent video view complete, like, long watch time, cold content delivery tasks respectively.
Codebook size
Recall
Semantic relevance
VVC
Like
LWT
CCD
I1 topic
I2 topic
10k
22.54% (-6.24%)
25.51% (-4.53%)
26.71% (-2.84%)
10.82% (-8.77%)
55.10% (+5.33%)
43.10% (+11.37%)
256 × 128
24.07% (+0.12%)
26.82% (+0.37%)
27.53% (+0.15%)
11.43% (-3.63%)
52.21% (-0.19%)
38.76% (+0.16%)
512 × 128 (default)
24.04%
26.72%
27.49%
11.86%
52.31%
38.70%
1024 × 256
23.18% (-3.58%)
25.97% (-2.81%)
27.15% (-1.24%)
11.98% (+1.01%)
52.73% (+0.80%)
39.49% (+2.04%)
Table 6. Effects of codebook size on MFLI’s performance. Note that VVC, Like, LWT, CCD represent video view complete, like, long watch time, cold content delivery tasks respectively.