Recent Papers of Recommendation Systems
2023 EVStore: Storage and Caching Capabilities for Scaling Embedding Tables in Deep Recommendation Systems ASPLOS'23 GRACE: A Scalable Graph-Based Approach To Accelerating Recommendation Model Inference ASPLOS'23 AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models OSDI'23 FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models arxiv 2022 RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure arxiv Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update OSDI'22 PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems arxiv HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework VLDB'22 HET-GMP: a graph-based system approach to scaling large embedding model training SIGMOD'22 Fleche: an efficient GPU embedding cache for personalized recommendations EuroSys'22 RecShard: statistical feature-based memory optimization for industry-scale neural recommendation ASPLOS'22 BagPipe: Accelerating Deep Recommendation Model Training arxiv 2021 Accelerating recommendation system training by leveraging popular choices VLDB'21 SPACE: Locality-Aware Processing in Heterogeneous Memory for Personalized Recommendations ISCA'21 Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models arxiv ISCA'22 RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance MICRO'21 2020 Kraken: Memory-Efficient Continual Learning for Large-Scale Real-Time Recommendations SC'20 Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems MLSys'20 DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference ISCA'20