Survey for Landing Generative AI in Social and E-commerce Recsys -- the Industry Perspectives
Not on the map. Why?
Abstract (arXiv)
Recently, generative AI (GAI), with their emerging capabilities, have presented unique opportunities for augmenting and revolutionizing industrial recommender systems (Recsys). Despite growing research efforts at the intersection of these fields, the integration of GAI into industrial Recsys remains in its infancy, largely due to the intricate nature of modern industrial Recsys infrastructure, operations, and product sophistication. Drawing upon our experiences in successfully integrating GAI into several major social and e-commerce platforms, this survey aims to comprehensively examine the underlying system and AI foundations, solution frameworks, connections to key research advancements, as well as summarize the practical insights and challenges encountered in the endeavor to integrate GAI into industrial Recsys. As pioneering work in this domain, we hope outline the representative developments of relevant fields, shed lights on practical GAI adoptions in the industry, and motivate future research.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 82 of its 138 references that are in the map. First posted on arXiv 10 Jun 2024.
Builds on
82 of 138 references.
References · 82
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
- 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
- 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
38 more
- 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
- 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
- 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
- 2019Language Models as Knowledge Bases?cited by 1,936
- 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
- 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
- 2021Improving language models by retrieving from trillions of tokenscited by 1,293
- 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
- 2019TinyBERT: Distilling BERT for Natural Language Understandingcited by 1,167
- 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
- 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
- 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
- 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 799
- 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
- 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
- 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
- 2020Scaling Laws for Autoregressive Generative Modelingcited by 522
- 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 518
- 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 492
- 2021BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generationcited by 412
- 2023A Survey on Large Language Models for Recommendationcited by 379
- 2023A Survey of Hallucination in Large Foundation Modelscited by 368
- 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 337
- 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
- 2021Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotationscited by 314
- 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 312
- 2019Q8BERT: Quantized 8Bit BERTcited by 300
- 2023How is ChatGPT's behavior changing over time?cited by 270
- 2023The False Promise of Imitating Proprietary LLMscited by 263
- 2020Identifying Mislabeled Data using the Area Under the Margin Rankingcited by 261
- 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructioncited by 248
- 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
- 2021Reinforcement learning based recommender systems: A surveycited by 188
50 most cited of 82 All 82 (JSON)
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.