Generative Representational Learning of Foundation Models for Recommendation | Paper Citation Record

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This paper cites On the Opportunities and Risks of Foundation Models.

Generative Representational Learning of Foundation Models for Recommendation On the Opportunities and Risks of Foundation Models

Reference 1

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Observation c3c2c31e-4dcf-47ac-8bfc-d872f5572e39 · outbound

This paper cites Language Models are Few-Shot Learners.

Generative Representational Learning of Foundation Models for Recommendation Language Models are Few-Shot Learners

Reference 2

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Observation 2e670991-2f03-4988-accc-669acc74dc62 · outbound

This paper cites Deep variational embedding representation on neural collaborative filtering for recommender systems.

Generative Representational Learning of Foundation Models for Recommendation Deep variational embedding representation on neural collaborative filtering for recommender systems

Reference 3

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Observation f31d06d7-0b89-4c17-9f6b-b88de350aad8 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Generative Representational Learning of Foundation Models for Recommendation Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 4

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Generative Representational Learning of Foundation Models for Recommendation Unresolved cited work

Reference 5

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Observation 0d8cd66f-5ea6-4575-81d1-e2775a0605c1 · outbound

This paper cites Sequence-to-sequence learning for review generation and recommendation.

Generative Representational Learning of Foundation Models for Recommendation Sequence-to-sequence learning for review generation and recommendation

Reference 6

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Observation f784a82e-dbb4-41dc-b17b-d4d79209bbdd · outbound

This paper cites Loramoe: Alleviate world knowledge forgetting in large language models via moe-style plugin, 2024.

Generative Representational Learning of Foundation Models for Recommendation Loramoe: Alleviate world knowledge forgetting in large language models via moe-style plugin, 2024

Reference 7

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Observation fc8ee5fc-33da-4d40-87aa-e1aa3749d131 · outbound

This paper cites Coba: Conver- gence balancer for multitask finetuning of large language models, 2024.

Generative Representational Learning of Foundation Models for Recommendation Coba: Conver- gence balancer for multitask finetuning of large language models, 2024

Reference 8

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Observation ff02c59f-1247-4d24-aba8-d894dab28de6 · outbound

This paper cites The llama 3 herd of models.

Generative Representational Learning of Foundation Models for Recommendation The llama 3 herd of models

Reference 9

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Observation 4b44d5e2-6d9e-45ec-b6ec-ead591d83404 · outbound

This paper cites AmazonQA: A Review-Based Question Answering Task.

Generative Representational Learning of Foundation Models for Recommendation AmazonQA: A Review-Based Question Answering Task

Reference 10

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Observation b6b98b22-6e72-45ca-9aa4-383341779989 · outbound

This paper cites Maxwell Harper and Joseph A.

Generative Representational Learning of Foundation Models for Recommendation Maxwell Harper and Joseph A

Reference 11

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Observation 98610ee1-b3bb-4555-a2de-edbade718d6d · outbound

This paper cites Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering.

Generative Representational Learning of Foundation Models for Recommendation Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering

Reference 12

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Observation 55dd1ee2-e66e-4b55-9f52-90b6fb19b49f · outbound

This paper cites Session-based recommendations with recurrent neural networks.

Generative Representational Learning of Foundation Models for Recommendation Session-based recommendations with recurrent neural networks

Reference 13

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Observation 23537826-49e3-4337-935d-b8636a4dada1 · outbound

This paper cites Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders.

Generative Representational Learning of Foundation Models for Recommendation Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Reference 14

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Observation 548f8d1c-7e96-43be-8ade-e82a9db2b414 · outbound

This paper cites Towards universal sequence representation learning for recommender systems.

Generative Representational Learning of Foundation Models for Recommendation Towards universal sequence representation learning for recommender systems

Reference 15

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Observation 82ffa8fe-97ce-4c6b-8b22-208895dcdb4d · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Generative Representational Learning of Foundation Models for Recommendation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 16

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Observation bfe89aee-5c16-439a-bded-0620bd36f54b · outbound

This paper cites Foundation Models for Recommender Systems: A Survey and New Perspectives.

Generative Representational Learning of Foundation Models for Recommendation Foundation Models for Recommender Systems: A Survey and New Perspectives

Reference 17

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Observation 93dd8b86-be05-4687-9c5f-534c787be1c9 · outbound

This paper cites A comprehensive survey on retrieval methods in recommender systems.

Generative Representational Learning of Foundation Models for Recommendation A comprehensive survey on retrieval methods in recommender systems

Reference 18

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Generative Representational Learning of Foundation Models for Recommendation Unresolved cited work

Reference 19

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This paper cites an unresolved cited work.

Generative Representational Learning of Foundation Models for Recommendation Unresolved cited work

Reference 20

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Observation e098bfc2-2805-4d4e-a5ca-0cf06c3cb5df · outbound

This paper cites Unsupervised tag recommendation for popular and cold products.

Generative Representational Learning of Foundation Models for Recommendation Unsupervised tag recommendation for popular and cold products

Reference 21

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Observation 93f52e2c-36b0-4dee-9f21-73d06d264ae0 · outbound

This paper cites Evaluation of entity resolution approaches on real-world match problems.

Generative Representational Learning of Foundation Models for Recommendation Evaluation of entity resolution approaches on real-world match problems

Reference 22

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Observation 71ceaaab-6aa9-4ea9-a3c1-8e92727f3585 · outbound

This paper cites Beyond distillation: Task-level mixture-of-experts for efficient inference, 2021.

Generative Representational Learning of Foundation Models for Recommendation Beyond distillation: Task-level mixture-of-experts for efficient inference, 2021

Reference 23

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Observation b65de887-38fd-49ca-ba26-57c96340df11 · outbound

This paper cites Supervised Transfer Learning for Product Information Question Answering.

Generative Representational Learning of Foundation Models for Recommendation Supervised Transfer Learning for Product Information Question Answering

Reference 24

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This paper cites UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models.

Generative Representational Learning of Foundation Models for Recommendation UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models

Reference 25

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Observation 2a6298a8-7fed-40ed-af9a-f1dfd8723f3d · outbound

This paper cites Ecomgpt: Instruction-tuning large language models with chain-of-task tasks for e-commerce.

Generative Representational Learning of Foundation Models for Recommendation Ecomgpt: Instruction-tuning large language models with chain-of-task tasks for e-commerce

Reference 26

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Observation b2862fc5-b4ab-49b2-bdb7-b2992fdc16d7 · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

Generative Representational Learning of Foundation Models for Recommendation Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 27

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Observation 91591517-6c9e-4db2-b10d-3eecd288186b · outbound

This paper cites Variational autoencoders for collaborative filtering.

Generative Representational Learning of Foundation Models for Recommendation Variational autoencoders for collaborative filtering

Reference 28

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Observation c7b277b7-b349-4f31-8565-c9c69b3205aa · outbound

This paper cites Clickprompt: Ctr models are strong prompt generators for adapting language models to ctr prediction.

Generative Representational Learning of Foundation Models for Recommendation Clickprompt: Ctr models are strong prompt generators for adapting language models to ctr prediction

Reference 29

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Observation 135cb032-19a5-45eb-88c0-503220c26530 · outbound

This paper cites How can recommender systems benefit from large language models: A survey.

Generative Representational Learning of Foundation Models for Recommendation How can recommender systems benefit from large language models: A survey

Reference 30

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This paper cites Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation.

Generative Representational Learning of Foundation Models for Recommendation Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation

Reference 31

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This paper cites Focal loss for dense object detection.

Generative Representational Learning of Foundation Models for Recommendation Focal loss for dense object detection

Reference 32

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This paper cites Optimizing Algorithms From Pairwise User Preferences.

Generative Representational Learning of Foundation Models for Recommendation Optimizing Algorithms From Pairwise User Preferences

Reference 33

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This paper cites Dora: Weight-decomposed low-rank adaptation, 2024.

Generative Representational Learning of Foundation Models for Recommendation Dora: Weight-decomposed low-rank adaptation, 2024

Reference 34

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Generative Representational Learning of Foundation Models for Recommendation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 35

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This paper cites Embedding in recommender systems: A survey.

Generative Representational Learning of Foundation Models for Recommendation Embedding in recommender systems: A survey

Reference 36

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This paper cites Decoupled Weight Decay Regularization.

Generative Representational Learning of Foundation Models for Recommendation Decoupled Weight Decay Regularization

Reference 37

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This paper cites Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models, 2024.

Generative Representational Learning of Foundation Models for Recommendation Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models, 2024

Reference 38

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This paper cites MuDoCo: Corpus for multidomain coreference resolution and referring expression generation.

Generative Representational Learning of Foundation Models for Recommendation MuDoCo: Corpus for multidomain coreference resolution and referring expression generation

Reference 39

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Observation df8f228f-9a4b-4e20-a586-225b4ecc1ef9 · outbound

This paper cites Image-based recommendations on styles and substitutes.

Generative Representational Learning of Foundation Models for Recommendation Image-based recommendations on styles and substitutes

Reference 40

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Observation 21c68ee4-a881-4c2e-98bc-08d08b3d15f8 · outbound

This paper cites Sfr-embedding-mistral: Enhance text retrieval with transfer learning.

Generative Representational Learning of Foundation Models for Recommendation Sfr-embedding-mistral: Enhance text retrieval with transfer learning

Reference 41

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Observation 7c95a7f0-9c86-4ee1-9fb7-0cfd66daeccd · outbound

This paper cites Efficient estimation of word representations in vector space.

Generative Representational Learning of Foundation Models for Recommendation Efficient estimation of word representations in vector space

Reference 42

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This paper cites Generative representational instruction tuning, 2025.

Generative Representational Learning of Foundation Models for Recommendation Generative representational instruction tuning, 2025

Reference 43

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Observation 0a436167-9974-4bfe-a249-5c4c0e6d4240 · outbound

This paper cites ecellm: Generalizing large language models for e-commerce from large-scale, high-quality instruction data.

Generative Representational Learning of Foundation Models for Recommendation ecellm: Generalizing large language models for e-commerce from large-scale, high-quality instruction data

Reference 44

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Observation 2da4de2a-fc9c-44f2-b460-cad0c3f8a5ab · outbound

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Generative Representational Learning of Foundation Models for Recommendation Unresolved cited work

Reference 45

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This paper cites Deep contextualized word representations.

Generative Representational Learning of Foundation Models for Recommendation Deep contextualized word representations

Reference 46

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This paper cites Improving language understanding by generative pre-training.

Generative Representational Learning of Foundation Models for Recommendation Improving language understanding by generative pre-training

Reference 47

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This paper cites Language models are unsupervised multitask learners.

Generative Representational Learning of Foundation Models for Recommendation Language models are unsupervised multitask learners

Reference 48

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Observation 93e7d3a3-2e01-4b8d-894e-5c3430574275 · outbound

This paper cites Benchmark datasets for entity resolution.

Generative Representational Learning of Foundation Models for Recommendation Benchmark datasets for entity resolution

Reference 49

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Observation 1ea4ca7e-c007-4493-b69c-2f41248b7424 · outbound

This paper cites Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search.

Generative Representational Learning of Foundation Models for Recommendation Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search

Reference 50

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Observation 2e88ce11-c940-4ff3-8195-d7094e2c6faa · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert- networks.

Generative Representational Learning of Foundation Models for Recommendation Sentence-bert: Sentence embeddings using siamese bert- networks

Reference 51

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Observation 8f592da3-1f57-4dbd-b641-38880c10287d · outbound

This paper cites STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural Videos.

Generative Representational Learning of Foundation Models for Recommendation STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural Videos

Reference 52

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T01:06:08.320768Z

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Observation 50288ca5-d734-43cd-9337-69636639fa15 · outbound

This paper cites Llama-e: Empowering e-commerce authoring with object-interleaved instruction following, 2024.

Generative Representational Learning of Foundation Models for Recommendation Llama-e: Empowering e-commerce authoring with object-interleaved instruction following, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:06:10.993879Z

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Observation 97f18f4b-7ce3-4118-ad13-6ec39dfafc75 · outbound

This paper cites Quality metrics in recommender systems: Do we calculate metrics consistently? In Proceedings of the 15th ACM conference on recommender systems, pages 708–713, 2021.

Generative Representational Learning of Foundation Models for Recommendation Quality metrics in recommender systems: Do we calculate metrics consistently? In Proceedings of the 15th ACM conference on recommender systems, pages 708–713, 2021

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:06:10.833300Z

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Observation 74382735-ce29-4a25-9de2-83121adceb87 · outbound

This paper cites Flip: Fine-grained alignment between id-based models and pretrained lan- guage models for ctr prediction.

Generative Representational Learning of Foundation Models for Recommendation Flip: Fine-grained alignment between id-based models and pretrained lan- guage models for ctr prediction

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:06:10.606871Z

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This paper cites Irgan: A minimax game for unifying generative and discriminative information retrieval models.

Generative Representational Learning of Foundation Models for Recommendation Irgan: A minimax game for unifying generative and discriminative information retrieval models

Reference 56

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Generative Representational Learning of Foundation Models for Recommendation Feature Allocation for Semantic Communication with Space-Time Importance Awareness

Reference 57

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This paper cites Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems.

Generative Representational Learning of Foundation Models for Recommendation Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems

Reference 58

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This paper cites Towards open-world recommendation with knowledge augmentation from large language models.

Generative Representational Learning of Foundation Models for Recommendation Towards open-world recommendation with knowledge augmentation from large language models

Reference 59

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Generative Representational Learning of Foundation Models for Recommendation Memocrs: Memory-enhanced sequential conversational recommender systems with large language models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:06:10.163565Z

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This paper cites Efficient and deployable knowledge infusion for open-world recommendations via large language models.

Generative Representational Learning of Foundation Models for Recommendation Efficient and deployable knowledge infusion for open-world recommendations via large language models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:06:09.948423Z

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This paper cites Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding.

Generative Representational Learning of Foundation Models for Recommendation Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding

Reference 62

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Generative Representational Learning of Foundation Models for Recommendation C-pack: Packaged resources to advance general chinese embedding, 2023

Reference 63

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This paper cites Scaling up open tagging from tens to thousands: Comprehension empowered attribute value extraction from product title.

Generative Representational Learning of Foundation Models for Recommendation Scaling up open tagging from tens to thousands: Comprehension empowered attribute value extraction from product title

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:06:09.761548Z

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This paper cites Ties-merging: Resolving interference when merging models.

Generative Representational Learning of Foundation Models for Recommendation Ties-merging: Resolving interference when merging models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:06:09.583368Z

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This paper cites Qwen2.5 Technical Report.

Generative Representational Learning of Foundation Models for Recommendation Qwen2.5 Technical Report

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T01:06:07.775029Z

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Generative Representational Learning of Foundation Models for Recommendation Unresolved cited work

Reference 67

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This paper cites Llasa: Large language and e-commerce shopping assistant, 2024.

Generative Representational Learning of Foundation Models for Recommendation Llasa: Large language and e-commerce shopping assistant, 2024

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:06:09.245476Z

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Observation 28332ea7-2835-4bef-afc2-3024b0d73407 · outbound

This paper cites A reduction of the $\theta(p_c) = 0$ problem to a conjectured inequality.

Generative Representational Learning of Foundation Models for Recommendation A reduction of the $\theta(p_c) = 0$ problem to a conjectured inequality

Reference 69

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Source-reported events for the cited work

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