Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation | Paper Citation Record

Observation 2c3f1378-3911-460c-ad3e-1dad8ceaa6a8 · outbound

This paper cites Autoregressive Entity Retrieval.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Autoregressive Entity Retrieval

Reference 1

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This paper cites Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5),.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5),

Reference 2

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This paper cites Recommender systems with generative retrieval,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Recommender systems with generative retrieval,

Reference 3

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This paper cites HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent

Reference 4

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This paper cites ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation

Reference 5

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This paper cites Onesug: The unified end-to-end generative framework for e- commerce query suggestion,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Onesug: The unified end-to-end generative framework for e- commerce query suggestion,

Reference 6

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This paper cites Learnable item tokenization for generative recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Learnable item tokenization for generative recommendation,

Reference 7

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This paper cites Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 8

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This paper cites Order-agnostic identifier for large language model-based generative recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Order-agnostic identifier for large language model-based generative recommendation,

Reference 9

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This paper cites Generative recommender with end-to-end learnable item tokenization,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Generative recommender with end-to-end learnable item tokenization,

Reference 10

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This paper cites Universal Item Tokenization for Transferable Generative Recommendation.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Universal Item Tokenization for Transferable Generative Recommendation

Reference 11

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Observation d74b3d13-503b-44d0-ac0b-ccb9caa11b8b · outbound

This paper cites Vqrae: Representation quantization autoencoders for mul- timodal understanding, generation and reconstruction,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Vqrae: Representation quantization autoencoders for mul- timodal understanding, generation and reconstruction,

Reference 12

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This paper cites Neural discrete representation learning,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Neural discrete representation learning,

Reference 13

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This paper cites Autoregressive image generation using residual quantization,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Autoregressive image generation using residual quantization,

Reference 14

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This paper cites Session-based Recommendations with Recurrent Neural Networks.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Session-based Recommendations with Recurrent Neural Networks

Reference 15

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This paper cites Personalized top-n sequential recommendation via convolutional sequence embedding,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Personalized top-n sequential recommendation via convolutional sequence embedding,

Reference 16

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This paper cites Self-attentive sequential recommenda- tion,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Self-attentive sequential recommenda- tion,

Reference 17

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This paper cites Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,

Reference 18

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Observation 44ffaa0c-e893-4501-a527-5ef75fb829b1 · outbound

This paper cites Feature-level deeper self-attention network for sequential recom- mendation.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Feature-level deeper self-attention network for sequential recom- mendation

Reference 19

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Observation 4213863e-bd9d-4876-8333-d77dc3f676cb · outbound

This paper cites S3-rec: Self-supervised learning for sequential recom- mendation with mutual information maximization,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation S3-rec: Self-supervised learning for sequential recom- mendation with mutual information maximization,

Reference 20

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Observation 4bd5d4f9-35ed-4fb7-8adb-9c8e48874abf · outbound

This paper cites Diffurec: A diffusion model for sequential recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Diffurec: A diffusion model for sequential recommendation,

Reference 21

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Observation 7b439541-0ca6-4ae4-aa9e-b66b108a6d1e · outbound

This paper cites Strec: Sparse transformer for sequential recommendations,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Strec: Sparse transformer for sequential recommendations,

Reference 22

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This paper cites Zero-shot recommendation: Towards class semantic relation learning for inferring labels of unseen micro-videos,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Zero-shot recommendation: Towards class semantic relation learning for inferring labels of unseen micro-videos,

Reference 23

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This paper cites Wide & deep learning for recommender systems,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Wide & deep learning for recommender systems,

Reference 24

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This paper cites Deep neural networks for youtube recommendations,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Deep neural networks for youtube recommendations,

Reference 25

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This paper cites DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 26

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This paper cites One model to rank them all: Unifying online advertising with end-to-end learning,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation One model to rank them all: Unifying online advertising with end-to-end learning,

Reference 27

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This paper cites M6-rec: Generative pretrained language models are open-ended recommender systems,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation M6-rec: Generative pretrained language models are open-ended recommender systems,

Reference 28

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Observation 4800448c-89e9-4341-b982-2d23097a5326 · outbound

This paper cites A Survey of Generative Search and Recommendation in the Era of Large Language Models.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation A Survey of Generative Search and Recommendation in the Era of Large Language Models

Reference 29

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This paper cites A bi-step grounding paradigm for large language mod- els in recommendation systems,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation A bi-step grounding paradigm for large language mod- els in recommendation systems,

Reference 30

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Observation d13a4fc1-de10-4c6d-9a60-2ae036224e0b · outbound

This paper cites Reinforced latent reasoning for llm-based recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Reinforced latent reasoning for llm-based recommendation,

Reference 31

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This paper cites MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender

Reference 32

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This paper cites How to index item ids for recommendation foundation models,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation How to index item ids for recommendation foundation models,

Reference 33

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This paper cites Learning to tokenize for generative retrieval,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Learning to tokenize for generative retrieval,

Reference 34

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This paper cites Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

Reference 35

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This paper cites OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment

Reference 36

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Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Onerec-think: In-text reasoning for generative recommendation,

Reference 37

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This paper cites Generating long semantic ids in parallel for recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Generating long semantic ids in parallel for recommendation,

Reference 38

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This paper cites Drift- aware continual tokenization for generative recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Drift- aware continual tokenization for generative recommendation,

Reference 39

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This paper cites Delrec: Distilling sequential pattern to enhance llms-based sequential recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Delrec: Distilling sequential pattern to enhance llms-based sequential recommendation,

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Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Reg4rec: Reasoning-enhanced genera- tive model for large-scale recommendation systems,

Reference 41

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This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 42

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This paper cites Longformer: The Long-Document Transformer.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Longformer: The Long-Document Transformer

Reference 43

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This paper cites Trie-aware transformers for generative recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Trie-aware transformers for generative recommendation,

Reference 44

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This paper cites Promise: Process reward models unlock test-time scaling laws in generative recommendations,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Promise: Process reward models unlock test-time scaling laws in generative recommendations,

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This paper cites APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization

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This paper cites Attention is all you need,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Attention is all you need,

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This paper cites Representation Learning with Contrastive Predictive Coding.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Representation Learning with Contrastive Predictive Coding

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

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,

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This paper cites Hierarchical gating networks for sequential recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Hierarchical gating networks for sequential recommendation,

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Observation ee555f7a-82db-496f-a02e-4f62ee63182f · outbound

This paper cites Plum: Adapting pre-trained lan- guage models for industrial-scale generative recommendations,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Plum: Adapting pre-trained lan- guage models for industrial-scale generative recommendations,

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Observation 97de7ed3-2854-4048-baec-a4c917b30476 · outbound

This paper cites Inductive representation learning on large graphs,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Inductive representation learning on large graphs,

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Observation 00ea743f-f7df-469e-9edf-c97bedd4ee09 · outbound

This paper cites Approximate nearest neighbor search under neural similarity metric for large-scale recommendation,.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Approximate nearest neighbor search under neural similarity metric for large-scale recommendation,

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