Matrix Factorization with Dynamic Multi-view Clustering for Recommender System | Paper Citation Record

Observation a78476bb-46af-4ee5-915e-5ce961ce8d00 · outbound

This paper cites Self-supervised learning for recommender systems: A survey,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Self-supervised learning for recommender systems: A survey,

Reference 1

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Observation b884752f-eead-4876-b271-e0e67a0b6b80 · outbound

This paper cites A survey of recommendation systems: recommendation models, techniques, and application fields,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System A survey of recommendation systems: recommendation models, techniques, and application fields,

Reference 2

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Observation 89cca3f9-8b4d-4a05-a3ab-cd81e84e56a1 · outbound

This paper cites Recommender systems in the era of large language models (llms),.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Recommender systems in the era of large language models (llms),

Reference 3

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Observation b97420f2-acb7-45d3-a4fd-c5edcb786558 · outbound

This paper cites Matrix factorization in recommender systems: algo- rithms, applications, and peculiar challenges,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Matrix factorization in recommender systems: algo- rithms, applications, and peculiar challenges,

Reference 4

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Observation fa19803c-8484-47a7-b232-256fd7c92564 · outbound

This paper cites Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems

Reference 5

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Observation 4eb3e716-67ac-437a-b1de-f847bb5b879b · outbound

This paper cites A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model

Reference 6

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Observation 841fd520-02fd-4212-a7c1-e72985491ee5 · outbound

This paper cites Surprise: A python library for recommender systems,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Surprise: A python library for recommender systems,

Reference 7

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Observation 477041d4-ab99-455b-a176-c18e8e5ca48d · outbound

This paper cites Matrix factorization techniques for recommender systems,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Matrix factorization techniques for recommender systems,

Reference 8

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Observation 414f5071-6c58-4984-8963-30f323648e03 · outbound

This paper cites Tt-svd: An efficient sparse decision-making model with two- way trust recommendation in the ai-enabled iot systems,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Tt-svd: An efficient sparse decision-making model with two- way trust recommendation in the ai-enabled iot systems,

Reference 9

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Observation 20b213e0-2bb9-4caf-b17e-b6542813f50f · outbound

This paper cites Self- adaptive deep asymmetric network for imbalanced recommendation,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Self- adaptive deep asymmetric network for imbalanced recommendation,

Reference 10

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Observation 34611728-c38a-4eef-86a1-cd206206e317 · outbound

This paper cites When federated recommendation meets cold-start problem: Separating item attributes and user interactions,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System When federated recommendation meets cold-start problem: Separating item attributes and user interactions,

Reference 11

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Observation 74e7dafc-3585-48ed-916b-3de81eb5da50 · outbound

This paper cites Personalized news recommen- dation: Methods and challenges,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Personalized news recommen- dation: Methods and challenges,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.396370Z

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Observation aab2a13a-0f4c-444f-9d8b-0d9a94d63606 · outbound

This paper cites Paper recommendation using specter with low-rank and sparse matrix factorization.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Paper recommendation using specter with low-rank and sparse matrix factorization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.381046Z

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Observation e3d93308-048b-43af-adf5-72c6f2e2bfc8 · outbound

This paper cites Contrastive knowledge amalgama- tion for unsupervised image classification,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Contrastive knowledge amalgama- tion for unsupervised image classification,

Reference 14

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This paper cites Collaborative knowledge amalgamation: Preserving discriminability and transferability in unsupervised learning,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Collaborative knowledge amalgamation: Preserving discriminability and transferability in unsupervised learning,

Reference 15

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Observation f83d3e3f-6941-4fdd-8f29-0bec333b2ccb · outbound

This paper cites Multi-view clustering via deep matrix factorization,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Multi-view clustering via deep matrix factorization,

Reference 16

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Observation 14c0e940-4ba3-4df7-846e-eb85853edecd · outbound

This paper cites A survey of graph neural network based recommendation in social networks,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System A survey of graph neural network based recommendation in social networks,

Reference 17

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Observation 630891d3-9b23-41c1-b69d-e4701115c8fe · outbound

This paper cites Dis- tribution knowledge embedding for graph pooling,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Dis- tribution knowledge embedding for graph pooling,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.303785Z

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Observation 409b8730-752c-47ae-b6ca-93afbd13ca24 · outbound

This paper cites Improving expressivity of gnns with subgraph-specific factor embedded normalization,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Improving expressivity of gnns with subgraph-specific factor embedded normalization,

Reference 19

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Observation 69433c85-170a-4aef-bcc1-e60934e5a745 · outbound

This paper cites Self-weighted contrastive fusion for deep multi-view clustering,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Self-weighted contrastive fusion for deep multi-view clustering,

Reference 20

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Observation 68c56c8d-a468-4226-994e-81f187c4e560 · outbound

This paper cites A generalized deep learning clustering algorithm based on non-negative matrix factorization,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System A generalized deep learning clustering algorithm based on non-negative matrix factorization,

Reference 21

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Observation c2598c44-46f9-4b34-a717-5b524f9cd01f · outbound

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

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 22

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Observation 530b4037-8c30-4730-a92c-3ac6af87a7d5 · outbound

This paper cites Dc- cnmf: Deep complementary and consensus non-negative matrix factor- ization for multi-view clustering,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Dc- cnmf: Deep complementary and consensus non-negative matrix factor- ization for multi-view clustering,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.243569Z

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Observation 69cd123a-0dfe-4875-9afc-c3c307742220 · outbound

This paper cites Interest-oriented Universal User Representation via Contrastive Learning.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Interest-oriented Universal User Representation via Contrastive Learning

Reference 24

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Observation ab4f96c7-6b76-4a26-b9a3-e1790716c34a · outbound

This paper cites An attention-based framework for multi-view clustering on grassmann manifold,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System An attention-based framework for multi-view clustering on grassmann manifold,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.227401Z

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Observation b4bcd0b0-88eb-4733-9abf-7abd06cb8f34 · outbound

This paper cites Learn- ing interest-oriented universal user representation via self-supervision,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Learn- ing interest-oriented universal user representation via self-supervision,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.211810Z

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Observation d733122b-61d9-4a14-9c4e-345b3f9e19c2 · outbound

This paper cites Empow- ering general-purpose user representation with full-life cycle behavior modeling,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Empow- ering general-purpose user representation with full-life cycle behavior modeling,

Reference 27

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Observation e3eebb83-55d1-4bcb-aae0-a2612999f621 · outbound

This paper cites Recommender systems survey,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Recommender systems survey,

Reference 28

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Observation 370238d9-139b-41c8-8240-4c84688225e3 · outbound

This paper cites Dnn-mf: Deep neural network matrix factorization approach for filtering information in multi-criteria recommender systems,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Dnn-mf: Deep neural network matrix factorization approach for filtering information in multi-criteria recommender systems,

Reference 29

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Observation f1beb133-68f9-4a5d-a260-8a9baf422eea · outbound

This paper cites Netflix update: Try this at home,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Netflix update: Try this at home,

Reference 30

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Observation 5ac2de97-7d7d-426a-abac-43f7dc274d2a · outbound

This paper cites Probabilistic matrix factorization,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Probabilistic matrix factorization,

Reference 31

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Observation aedc5704-1ccf-4eff-a759-7e8cb33bca58 · outbound

This paper cites Lightfr: Lightweight federated recommendation with privacy-preserving matrix factorization,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Lightfr: Lightweight federated recommendation with privacy-preserving matrix factorization,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.130321Z

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Observation 2aa1d75d-77ba-4202-aea8-64cfcc60aaa6 · outbound

This paper cites Robust multi-view clustering with noisy correspondence,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Robust multi-view clustering with noisy correspondence,

Reference 33

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Observation 5960739c-23a0-4eac-ab4b-ea31cd591f42 · outbound

This paper cites Prototype matching learning for incomplete multi-view clustering,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Prototype matching learning for incomplete multi-view clustering,

Reference 34

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Observation f805ccb7-0471-452e-814f-be61997775ce · outbound

This paper cites A survey of visual transformers,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System A survey of visual transformers,

Reference 35

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Observation 0a6def24-7f7c-49e7-97aa-fb5b8917e78e · outbound

This paper cites Better integrating vision and semantics for improving few-shot classification,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Better integrating vision and semantics for improving few-shot classification,

Reference 36

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Observation fab86007-21a2-4375-9a88-e26e1813eb75 · outbound

This paper cites Fewvs: A vision-semantics integration framework for few-shot image classification,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Fewvs: A vision-semantics integration framework for few-shot image classification,

Reference 37

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Observation 43068e15-097a-4b2b-ab31-5eba5579896a · outbound

This paper cites Everyone’s preference changes differently: A weighted multi-interest model for retrieval,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Everyone’s preference changes differently: A weighted multi-interest model for retrieval,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.043460Z

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Observation 26bbe281-c865-45f3-b830-95cd623f70d0 · outbound

This paper cites Learning to rank features for recommendation over multiple categories,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Learning to rank features for recommendation over multiple categories,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:12.012799Z

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Observation 991ad5cb-0de6-484f-adba-92b13591ec03 · outbound

This paper cites Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:11.996183Z

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Observation 4a48a87c-abb6-460b-a5d7-28ff263adad7 · outbound

This paper cites Explainable matrix factorization for collaborative filtering,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Explainable matrix factorization for collaborative filtering,

Reference 42

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This paper cites Visual interpretability of image-based classification models by generative latent space dis- entanglement applied to in vitro fertilization,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Visual interpretability of image-based classification models by generative latent space dis- entanglement applied to in vitro fertilization,

Reference 43

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Observation 436b57b0-3e84-4257-a588-3cd8c0618f7c · outbound

This paper cites Visual interpretability of bioimaging deep learning models,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Visual interpretability of bioimaging deep learning models,

Reference 44

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Observation 3ecb38e1-d113-4bea-a3b5-c3cc510ecf5e · outbound

This paper cites The movielens datasets: History and context,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System The movielens datasets: History and context,

Reference 45

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Observation 43077fd9-0681-4592-baa1-643fdc56195f · outbound

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

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:11.945967Z

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Observation 1e8f08dd-9f10-4058-9047-9f38c6fea509 · outbound

This paper cites Improving recommendation lists through topic diversification,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Improving recommendation lists through topic diversification,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:11.930466Z

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Observation 3fd79abc-c033-48f5-872b-25953112d50c · outbound

This paper cites Eigentaste: A constant time collaborative filtering algorithm,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Eigentaste: A constant time collaborative filtering algorithm,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:11.914173Z

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Observation d1428a63-cd40-427c-82b2-b4768db4c92d · outbound

This paper cites Optuna: A next- generation hyperparameter optimization framework,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Optuna: A next- generation hyperparameter optimization framework,

Reference 49

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Observation d9bf8d82-e233-4ed4-bc9a-67aeed6d3004 · outbound

This paper cites Neural Network Matrix Factorization.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Neural Network Matrix Factorization

Reference 50

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Observation 603fc76d-f111-4dd2-824a-381f28e7381f · outbound

This paper cites A comprehensive survey of evaluation techniques for recommendation systems,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System A comprehensive survey of evaluation techniques for recommendation systems,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:11.888226Z

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Observation 047a4ad8-bb96-4565-aacd-63aa1f5433f2 · outbound

This paper cites Glocal-k: Global and local kernels for recommender systems,.

Matrix Factorization with Dynamic Multi-view Clustering for Recommender System Glocal-k: Global and local kernels for recommender systems,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:11.871705Z

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