REVIEW 23 cited by
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
read the original abstract
Learning feature interactions is important to the model performance of online advertising services. As a result, extensive efforts have been devoted to designing effective architectures to learn feature interactions. However, we observe that the practical performance of those designs can vary from dataset to dataset, even when the order of interactions claimed to be captured is the same. That indicates different designs may have different advantages and the interactions captured by them have non-overlapping information. Motivated by this observation, we propose DHEN - a deep and hierarchical ensemble architecture that can leverage strengths of heterogeneous interaction modules and learn a hierarchy of the interactions under different orders. To overcome the challenge brought by DHEN's deeper and multi-layer structure in training, we propose a novel co-designed training system that can further improve the training efficiency of DHEN. Experiments of DHEN on large-scale dataset from CTR prediction tasks attained 0.27\% improvement on the Normalized Entropy (NE) of prediction and 1.2x better training throughput than state-of-the-art baseline, demonstrating their effectiveness in practice.
Forward citations
Cited by 23 Pith papers
-
Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators
A Triton compiler backend, TorchInductor adaptations, and small language extensions let Meta's MTIA-2i run Triton kernels competitively with expert-tuned C++ in production.
-
ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation
ROCS restructures recommendation models so user-side computation is shared across all candidate items, yielding up to 3x serving throughput at equal or better prediction quality.
-
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction
LoopCTR trains CTR models with recursive layer reuse and process supervision so that zero-loop inference outperforms baselines on public and industrial datasets.
-
Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation
Releases TencentGR-1M and TencentGR-10M datasets with baselines for all-modality generative recommendation in advertising, including weighted evaluation for conversions.
-
HCCL: Collective Communication for Meta Training and Inference Accelerators
HCCL offloads collective communication to MTIA 300's message engines, achieving up to 940 GB/s intra-rack bandwidth and sub-6µs latency for inference.
-
Probabilistic Residual Learning for Online Recommendations
PRL adds a cluster-aware, causality-adjusted residual correction layer to any base recommender, improving cold-start cross-domain recommendation accuracy in experiments.
-
UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction
UniRank is an open benchmark that standardizes chronological autoregressive supervision, multi-task evaluation, and capacity controls for 15 unified ranking models on five large datasets.
-
Bumblebee: Interleaved Mixed-Layer Building Blocks for Large-Scale Recommendation Systems
Interleaving sequence modeling with feature interaction in repeated blocks improves recommendation accuracy by about 0.2-1.4% NE over sequential baselines at matched parameter counts.
-
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
LoKA enables practical FP8 use in numerically sensitive large recommendation models via profiling, model adaptations, and runtime kernel orchestration.
-
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
LoKA enables practical FP8 use in numerically sensitive large recommendation models via online profiling of activations, reusable model modifications for stability, and dynamic kernel dispatching.
-
Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on...
-
SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling
Speculative precomputation of foundation-model user–item embeddings decouples heavy inference from the serving path and yields 0.67% revenue gain at Meta ads scale.
-
FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation
FLAME condenses ensemble diversity into a single network via modular ensemble simulation and guided mutual learning during training, delivering ensemble-level performance with single-network inference speed on sequent...
-
MixFormer: Co-Scaling Up Dense and Sequence in Industrial Recommenders
MixFormer unifies dense feature interaction and user-sequence modeling in a single Transformer-style backbone with a user-item decoupling speedup, reporting accuracy and efficiency gains over stacked and parallel reco...
-
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
An agentic kernel-coding system combining tree search with hardware-knowledge retrieval generated optimized Triton kernels for NVIDIA, AMD, and Meta's MTIA accelerators: 100% correctness on 480 operator-platform confi...
-
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
PyTorch Fully Sharded Data Parallel enables training of significantly larger models than Distributed Data Parallel with comparable speed and near-linear TFLOPS scaling.
-
Optimus: A Generic Operator-Level PyTorch Model Transformation Framework
Optimus rewrites atomic operator patterns in PT2 graphs via greedy search, delivering large QPS, memory, and compile-time gains on production recommendation models.
-
GR2 Technical Report
GR2 applies mid-training on semantic IDs, reasoning distillation, RL with conditional verifiable rewards, and a context compressor to re-ranking in industrial recsys, reporting +18.7% R@1 over baselines.
-
DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors
DeMix diagnoses mixed error types in training data via influence-vector-based multi-label classification with an intervention strategy, reporting 22.61% F1 gain and 9.32% downstream improvement on 11 tasks.
-
Memento: Personalized RAG-Style Long-Retention Data Scaling for META Ads Recommendation
Memento applies personalized RAG-style retrieval to long user history for Meta ads models, delivering 5-10x efficiency, sub-10ms latency, and 1% CTR / 1.2% CVR lifts in production.
-
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
LoKA claims to make FP8 practical for large recommendation models via statistical probing, model adaptations, and accuracy-aware kernel dispatch.
-
SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling
SOLARIS speculatively precomputes user-item latent representations to decouple large-model inference from real-time serving, delivering 0.67% revenue gain when deployed in Meta's ad system.
-
On the Practice of Scaling Search Conversion Rate Prediction
Empirical scaling of backbone, embeddings, and data shows largely independent additive gains, enabling a deployed model with 2.5x data and 8x compute that delivers +2.6% CVR improvement with minimal latency change.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.