QGS introduces query-item pair encoding and query-conditioned prediction with a linear HSTU encoder and HFG-Attention to reduce noise from query switches in generative search ranking, reporting online gains in a commercial system.
hub Canonical reference
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Canonical reference. 83% of citing Pith papers cite this work as background.
26
Pith papers citing it
Background
83% of classified citations
abstract
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide \& Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.
hub tools
citation-role summary
background 5
method 1
citation-polarity summary
representative citing papers
NSGR is a tree-structured generative reranker that progressively generates optimal lists via next-scale expansion and multi-scale neighbor loss to balance perspectives and align training signals.
SCALR generates synthetic cross-domain events to augment recommendation training data and reports statistically significant improvements in industrial A/B tests.
GenLI generates diverse target-independent interest distributions via an IGM, retrieves behaviors with O(1) lookup in BRM, and fuses via IFM gating to balance accuracy and efficiency in CTR prediction.
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
PDQUBO is a new performance-driven QUBO method for feature selection in recommender systems that incorporates counterfactual performance impacts of features and pairs, is model-agnostic, and outperforms prior quantum and some classical baselines on CTR tasks.
DNNs mitigate dimensional collapse of embeddings in feature interaction models, shown via parallel and stacked experiments plus gradient analysis.
LLMs exhibit mid-layer representation advantage for recommendations; MARC compresses representations modularly to reduce costs while improving performance, as shown in a large-scale online advertising deployment.
MBGR is a new generative recommendation framework using business-aware semantic IDs, multi-business prediction, and label dynamic routing to handle multiple businesses without seesaw effects or representation confusion, validated by experiments and deployed at Meituan.
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
DMF adds target-aware bridging features and an inference-optimized decoupled attention layer to combine modality-centric and modality-enriched user interest modeling for CTR prediction.
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.
CCNETS is a new modular causal framework using three cooperative modules and a Zoint mechanism to align synthetic data generation with classifier needs on imbalanced pattern recognition tasks.
Cloudless-Training proposes a two-layer serverless framework with elastic scheduling and two new synchronization strategies (ASGD-GA and inter-PS model averaging) that reports 9.2-24% cost reduction and up to 1.7x speedup for geo-distributed PS-based ML training.
PACEvolve++ uses a phase-adaptive reinforcement learning advisor to decouple hypothesis selection from execution in LLM-driven evolutionary search, delivering faster convergence than prior frameworks on load balancing, recommendation, and protein tasks.
Light-FMP prunes features and model parameters in deep recommender systems by pretraining a hard-concrete masking layer on data subsets, then retraining the reduced model to improve both efficiency and accuracy over prior methods.
SSR uses static random filters and iterative competitive sparse mechanisms to explicitly enforce sparsity in recommendation models, outperforming dense baselines on public and billion-scale industrial datasets.
OneRec unifies retrieval and ranking in a generative recommender using session-wise decoding and iterative DPO-based preference alignment, achieving real-world gains on Kuaishou.
UniScale couples entire-space data construction with a hierarchical fusion transformer to improve scaling behavior and deliver 1.70% purchase and 2.04% GMV lifts in large-scale e-commerce search A/B tests.
A unified F-statistic screening and weighted evaluation method prunes both unstructured and structured parameters in FNNs and CNNs, claiming order-of-magnitude size reduction with competitive accuracy on vision datasets.
FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.
MACDAE infers implicit contexts via a constrained autoencoder and integrates them into an end-to-end O2O recommender, reporting gains on Yelp/Dianping/Koubei and 2.9%/5.6% lifts in online CTR/conversion.
RecGPT-Mobile runs a compact LLM on phones to understand evolving user intent from behaviors and improve mobile e-commerce recommendations.
DSAIN introduces situational features and tri-directional fusion to enhance behavior sequence modeling for CTR prediction, delivering 2.7% CTR, 2.62% CPM, and 2.16% GMV lifts in online A/B tests on the Meituan platform.
citing papers explorer
-
Retrieval-Augmented Generation with Graphs (GraphRAG)
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.