Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks | Paper Citation Record

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This paper cites Matrix factorization techniques for recommender systems,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Matrix factorization techniques for recommender systems,

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This paper cites Spectral regularization algorithms for learning large incomplete matrices,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Spectral regularization algorithms for learning large incomplete matrices,

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This paper cites Neural collab- orative filtering,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Neural collab- orative filtering,

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This paper cites Autorec: Au- toencoders meet collaborative filtering,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Autorec: Au- toencoders meet collaborative filtering,

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This paper cites Collaborative filtering for implicit feedback datasets,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Collaborative filtering for implicit feedback datasets,

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This paper cites Scalable linear shallow autoencoder for collaborative filtering,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Scalable linear shallow autoencoder for collaborative filtering,

Reference 6

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This paper cites Unifying explicit and implicit feedback for collaborative filtering,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Unifying explicit and implicit feedback for collaborative filtering,

Reference 7

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This paper cites Unifying explicit and implicit feedback for rating prediction and ranking recommendation tasks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Unifying explicit and implicit feedback for rating prediction and ranking recommendation tasks,

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This paper cites Lightgcn: Simplifying and powering graph convolution network for recommenda- tion,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Lightgcn: Simplifying and powering graph convolution network for recommenda- tion,

Reference 9

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This paper cites Neural graph collaborative filtering,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Neural graph collaborative filtering,

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This paper cites Inductive matrix completion based on graph neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Inductive matrix completion based on graph neural networks,

Reference 11

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This paper cites Explicit feedbacks meet with implicit feedbacks: A combined approach for recommendation system,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Explicit feedbacks meet with implicit feedbacks: A combined approach for recommendation system,

Reference 12

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This paper cites Unifying explicit and implicit feedback for rating prediction and ranking recommendation tasks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Unifying explicit and implicit feedback for rating prediction and ranking recommendation tasks,

Reference 13

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This paper cites Probabilistic matrix factoriza- tion,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Probabilistic matrix factoriza- tion,

Reference 14

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This paper cites Providing reliability in recommender systems through bernoulli matrix factorization,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Providing reliability in recommender systems through bernoulli matrix factorization,

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This paper cites Generalized probabilistic matrix factor- izations for collaborative filtering,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Generalized probabilistic matrix factor- izations for collaborative filtering,

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This paper cites Scalable recommendation with hierarchical poisson factorization,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Scalable recommendation with hierarchical poisson factorization,

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This paper cites BPR: bayesian personalized ranking from implicit feedback,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks BPR: bayesian personalized ranking from implicit feedback,

Reference 18

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This paper cites Neural Network Matrix Factorization.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Neural Network Matrix Factorization

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This paper cites Comparative convolu- tional dynamic multi-attention recommendation model,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Comparative convolu- tional dynamic multi-attention recommendation model,

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This paper cites Kernelized deep learning for matrix factorization recommendation system using explicit and implicit information,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Kernelized deep learning for matrix factorization recommendation system using explicit and implicit information,

Reference 21

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This paper cites Collaborative denoising auto-encoders for top-n recommender systems,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Collaborative denoising auto-encoders for top-n recommender systems,

Reference 22

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This paper cites Variational autoencoders for collaborative filtering,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Variational autoencoders for collaborative filtering,

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This paper cites Bilateral variational au- toencoder for collaborative filtering,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Bilateral variational au- toencoder for collaborative filtering,

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This paper cites Representation learn- ing: serial-autoencoder for personalized recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Representation learn- ing: serial-autoencoder for personalized recommendation,

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Observation 1194bc83-02e4-49a4-9edc-5b077420c41e · outbound

This paper cites Convolutional mat- rix factorization for document context-aware recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Convolutional mat- rix factorization for document context-aware recommendation,

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This paper cites Collaborative deep learning for recommender systems,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Collaborative deep learning for recommender systems,

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This paper cites Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation

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Observation 8e53efff-d9df-46dc-9dac-23b3fc17456d · outbound

This paper cites Knowledge graphs and pretrained language models enhanced representation learning for conversational recommender systems,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Knowledge graphs and pretrained language models enhanced representation learning for conversational recommender systems,

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This paper cites Vbpr: Visual bayesian personalized ranking from implicit feedback.,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Vbpr: Visual bayesian personalized ranking from implicit feedback.,

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This paper cites Graph convolution network based recommender systems: Learning guarantee and item mixture powered strategy,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Graph convolution network based recommender systems: Learning guarantee and item mixture powered strategy,

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This paper cites Graph convolutional adversarial networks for spatiotemporal anomaly detection,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Graph convolutional adversarial networks for spatiotemporal anomaly detection,

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This paper cites Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

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This paper cites Multi-behavior graph neural networks for recommender system,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Multi-behavior graph neural networks for recommender system,

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This paper cites Siren: Sign-aware recommendation using graph neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Siren: Sign-aware recommendation using graph neural networks,

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This paper cites Diversify- ing collaborative filtering via graph spreading network and selective sampling,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Diversify- ing collaborative filtering via graph spreading network and selective sampling,

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This paper cites Trustgnn: Graph neural network-based trust evaluation via learnable propagative and composable nature,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Trustgnn: Graph neural network-based trust evaluation via learnable propagative and composable nature,

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This paper cites On deep learning for trust-aware recommendations in social networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks On deep learning for trust-aware recommendations in social networks,

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Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Rethink- ing missing data: Aleatoric uncertainty-aware recommendation,

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This paper cites Uncertainty-adjusted recommend- ation via matrix factorization with weighted losses,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Uncertainty-adjusted recommend- ation via matrix factorization with weighted losses,

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This paper cites Federated learning enabled graph convolutional autoencoder and factorization machine for po- tential friendship prediction in social networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Federated learning enabled graph convolutional autoencoder and factorization machine for po- tential friendship prediction in social networks,

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This paper cites Multi- view enhanced graph attention network for session-based music re- commendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Multi- view enhanced graph attention network for session-based music re- commendation,

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This paper cites Intent-aware graph neural network for point-of-interest embedding and recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Intent-aware graph neural network for point-of-interest embedding and recommendation,

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This paper cites Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction

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This paper cites Multi-knowledge enhanced graph convolution for learning resource recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Multi-knowledge enhanced graph convolution for learning resource recommendation,

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This paper cites FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer

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This paper cites Knowledge-guided article embedding refinement for session-based news recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Knowledge-guided article embedding refinement for session-based news recommendation,

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This paper cites Music recommendation via hypergraph embedding,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Music recommendation via hypergraph embedding,

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This paper cites Modeling self-representation label correlations for textual aspects and emojis recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Modeling self-representation label correlations for textual aspects and emojis recommendation,

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This paper cites Category-aware self- supervised graph neural network for session-based recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Category-aware self- supervised graph neural network for session-based recommendation,

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This paper cites Dynamically expandable graph convolution for streaming recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Dynamically expandable graph convolution for streaming recommendation,

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This paper cites A survey on reinforcement learning for recommender systems,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks A survey on reinforcement learning for recommender systems,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:44.661547Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7376dc93-03e4-4511-a00b-656bbcfb1694 · outbound

This paper cites Plug-and-play model-agnostic counterfactual policy synthesis for deep reinforcement learning-based recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Plug-and-play model-agnostic counterfactual policy synthesis for deep reinforcement learning-based recommendation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:44.530967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c83d8e2a-9724-4995-9396-b1ab1d2e29d4 · outbound

This paper cites Dynamic and static representation learning network for recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Dynamic and static representation learning network for recommendation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:44.341484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30dcdad6-f983-4c9a-b1e4-bc98369081b3 · outbound

This paper cites Time interval- enhanced graph neural network for shared-account cross-domain se- quential recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Time interval- enhanced graph neural network for shared-account cross-domain se- quential recommendation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:44.132061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5ed468f9-d034-4870-ace3-caf27c0dc447 · outbound

This paper cites Tea: A sequential recommendation framework via temporally evolving aggregations,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Tea: A sequential recommendation framework via temporally evolving aggregations,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:43.995869Z

Source-reported events for the cited work

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Observation d34f3acf-e827-47e0-a511-8d4a19dbc0b8 · outbound

This paper cites A survey on federated recommendation systems,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks A survey on federated recommendation systems,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:43.820591Z

Source-reported events for the cited work

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Observation a8a4e19f-0762-420b-bbba-2662f292eec3 · outbound

This paper cites Privfr: Privacy-enhanced feder- ated recommendation with shared hash embedding,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Privfr: Privacy-enhanced feder- ated recommendation with shared hash embedding,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:43.687364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc767ca1-f28e-43b7-9169-bb323567bfdd · outbound

This paper cites Estimating and evaluating the uncertainty of rating predictions and top-n recommendations in recommender systems,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Estimating and evaluating the uncertainty of rating predictions and top-n recommendations in recommender systems,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:43.485747Z

Source-reported events for the cited work

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Observation 17b3a7c2-9c66-453d-aabe-2ab200513e21 · outbound

This paper cites Ordrec: An ordinal model for predicting personalized item rating distributions,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Ordrec: An ordinal model for predicting personalized item rating distributions,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:43.342662Z

Source-reported events for the cited work

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Observation 4aa97e04-39d6-4a4e-991a-c8dbe240636f · outbound

This paper cites Modeling user rating profiles for collaborative filter- ing,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Modeling user rating profiles for collaborative filter- ing,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:43.211217Z

Source-reported events for the cited work

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Observation 89466548-1289-49ad-b563-39a3c432eb8e · outbound

This paper cites An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyond.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyond

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T22:15:28.785616Z

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Observation f2cc00b4-843d-41b4-907e-22937811d62a · outbound

This paper cites Explainable recommendation via interpretable feature mapping and evaluation of explainability,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Explainable recommendation via interpretable feature mapping and evaluation of explainability,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:42.950058Z

Source-reported events for the cited work

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Observation 46cfd070-6a6d-45e6-b2fd-5ef7e3227365 · outbound

This paper cites The you- tube video recommendation system,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks The you- tube video recommendation system,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:42.778060Z

Source-reported events for the cited work

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Observation 9f6ca544-bb2e-4a0f-bac7-941766759a07 · outbound

This paper cites Explainable recommendation: A survey and new perspectives,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Explainable recommendation: A survey and new perspectives,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:42.586738Z

Source-reported events for the cited work

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Observation 2a2a2058-f71d-414a-933a-ddb99adebe45 · outbound

This paper cites Matrix completion with the trace norm: Learning, bounding, and transducing,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Matrix completion with the trace norm: Learning, bounding, and transducing,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:42.407386Z

Source-reported events for the cited work

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Observation c92239bb-c96d-4766-a719-636707d1df0b · outbound

This paper cites Speedup matrix completion with side information: Application to multi-label learning,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Speedup matrix completion with side information: Application to multi-label learning,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:42.179316Z

Source-reported events for the cited work

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Observation 96822cad-97bd-48a3-9094-36c6e64623f3 · outbound

This paper cites A pac-bayesian approach to generalization bounds for graph neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks A pac-bayesian approach to generalization bounds for graph neural networks,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:41.972011Z

Source-reported events for the cited work

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Observation baea64c8-3fb2-49f0-be1c-23f86df42e2a · outbound

This paper cites Stability and generalization of graph convolutional neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Stability and generalization of graph convolutional neural networks,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T22:15:29.311262Z

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Observation dfcb7397-dd14-469e-97e1-ec4b046fd8d9 · outbound

This paper cites Generalization bounds for graph convolutional neural networks via Rademacher complexity.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Generalization bounds for graph convolutional neural networks via Rademacher complexity

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T22:15:29.396923Z

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Observation 90412048-48ef-4f5c-a916-b379c8503f78 · outbound

This paper cites Learning the- ory can (sometimes) explain generalisation in graph neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Learning the- ory can (sometimes) explain generalisation in graph neural networks,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:41.725241Z

Source-reported events for the cited work

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Observation 05c5b85f-96cc-4185-8b36-d71bec38a646 · outbound

This paper cites Foundations and Frontiers of Graph Learning Theory.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Foundations and Frontiers of Graph Learning Theory

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:15:33.450341Z

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Observation a0ca3da6-20c1-4a8e-8154-5d90e8cbaa64 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Spectrally-normalized margin bounds for neural networks,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:41.296670Z

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Observation 9c180d38-0faf-48a5-bac7-622fa7e41cc4 · outbound

This paper cites Size-free generalization bounds for con- volutional neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Size-free generalization bounds for con- volutional neural networks,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:41.029469Z

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Observation 4b96992b-b805-4bc0-8061-7c99790b7100 · outbound

This paper cites On measuring excess capacity in neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks On measuring excess capacity in neural networks,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:40.784034Z

Source-reported events for the cited work

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Observation 6d2d176a-1f5f-400f-bffc-9c4f0e24f69a · outbound

This paper cites Norm-based general- isation bounds for deep multi-class convolutional neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Norm-based general- isation bounds for deep multi-class convolutional neural networks,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:40.543731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eb0f42aa-e03e-4842-8136-7fc8bd0a5009 · outbound

This paper cites Neural tangent kernel: Conver- gence and generalization in neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Neural tangent kernel: Conver- gence and generalization in neural networks,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:40.347275Z

Source-reported events for the cited work

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Observation 8b746b09-1a89-49c5-adea-7432acead39b · outbound

This paper cites Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:40.166321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b1a5118f-cb18-4931-b779-58a9489053e4 · outbound

This paper cites Gradient descent prov- ably optimizes over-parameterized neural networks,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Gradient descent prov- ably optimizes over-parameterized neural networks,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:39.976331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 86537b8a-69ff-4991-a4f1-b76e7e09b5c7 · outbound

This paper cites Generalization bounds for unsupervised and semi-supervised learning with autoencoders,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Generalization bounds for unsupervised and semi-supervised learning with autoencoders,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:39.795848Z

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Observation 7943c81d-ab03-494f-9eed-8420f5db2889 · outbound

This paper cites Lp-norm Sauer–Shelah lemma for margin multi- category classifiers,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Lp-norm Sauer–Shelah lemma for margin multi- category classifiers,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:39.577476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ad51b654-83db-4f09-90f1-dd6d9175e430 · outbound

This paper cites Rademacher complexity and generalization performance of multi-category margin classifiers,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Rademacher complexity and generalization performance of multi-category margin classifiers,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:39.417297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aacc2135-2449-449d-ba0c-73858c47469a · outbound

This paper cites Vc theory of large margin multi-category classifiers.,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Vc theory of large margin multi-category classifiers.,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:39.172248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c57855bf-5eb1-431f-9a4f-1bf766d7b300 · outbound

This paper cites Implicit bias of large depth networks: a notion of rank for nonlinear functions,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Implicit bias of large depth networks: a notion of rank for nonlinear functions,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:38.993313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 774fca9b-9bbe-4a18-b157-0ed2f9868e31 · outbound

This paper cites Generalization analysis of deep non-linear matrix completion,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Generalization analysis of deep non-linear matrix completion,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:38.806793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8c1634b8-f582-4823-b53f-7eaad5d7b5bf · outbound

This paper cites Implicit bias of sgd inl_2-regularized linear dnns: One-way jumps from high to low rank,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Implicit bias of sgd inl_2-regularized linear dnns: One-way jumps from high to low rank,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:38.597824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 770b0bfe-8520-4885-817a-1b99766c30e5 · outbound

This paper cites Multi-class svms: From tighter data-dependent generalization bounds to novel algorithms,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Multi-class svms: From tighter data-dependent generalization bounds to novel algorithms,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:38.417768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 081e13b9-0d26-4496-9d30-a44b3d5bf133 · outbound

This paper cites Fine-grained generalization analysis of vector-valued learning,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Fine-grained generalization analysis of vector-valued learning,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:38.241340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e7810a0-9161-460a-bbe2-4919b06e3004 · outbound

This paper cites Fine-grained gener- alization analysis of structured output prediction,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Fine-grained gener- alization analysis of structured output prediction,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:38.050937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c679d02c-6203-4b9b-b1a5-9a559886b3b3 · outbound

This paper cites Matrix completion and low-rank svd via fast alternating least squares,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Matrix completion and low-rank svd via fast alternating least squares,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:37.857894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ff984c91-3e54-4179-8af5-6f8d0a59c052 · outbound

This paper cites Orthogonal inductive matrix completion,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Orthogonal inductive matrix completion,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:37.648051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 558a729d-cfdc-4f71-a506-f09ae4f152a2 · outbound

This paper cites Xsimgcl: Towards extremely simple graph contrastive learning for recommenda- tion,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Xsimgcl: Towards extremely simple graph contrastive learning for recommenda- tion,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:37.455467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6ffcbfd6-8e8a-4c6d-b6cc-9ad7c3954bf5 · outbound

This paper cites Dtcdr: A framework for dual-target cross-domain recommendation,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Dtcdr: A framework for dual-target cross-domain recommendation,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:37.211396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 309336c5-63a6-4e20-a9a2-f823ec3c2617 · outbound

This paper cites Justifying recommendations using distantly-labeled reviews and fine-grained aspects,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Justifying recommendations using distantly-labeled reviews and fine-grained aspects,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:37.011035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 70e1a03f-f745-4b6f-9a78-699e620c93ee · outbound

This paper cites High-dimensional probability,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks High-dimensional probability,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:36.816434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c9881a8e-2898-45e7-a5d2-aa8875327740 · outbound

This paper cites Covering number bounds of certain regularized linear function classes,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Covering number bounds of certain regularized linear function classes,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:36.591002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation acf66231-3714-40c1-97c4-4c601a0b701b · outbound

This paper cites Collaborative filtering with the trace norm: Learning, bounding, and transducing,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Collaborative filtering with the trace norm: Learning, bounding, and transducing,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:36.499224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 75e3b6bd-e3e9-4e6a-9ad1-464c96b4b173 · outbound

This paper cites Matrix reconstruction with the local max norm,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Matrix reconstruction with the local max norm,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:36.331827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c9e46f6d-c65e-4415-9a61-2626332abfd7 · outbound

This paper cites Learning with the weighted trace-norm under arbitrary sampling distributions,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Learning with the weighted trace-norm under arbitrary sampling distributions,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:36.168796Z

Source-reported events for the cited work

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Observation 69c2e965-5751-4c7c-a4a9-1bf0c6225d2e · outbound

This paper cites Using side information to reliably learn low-rank matrices from missing and corrupted obser- vations,.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks Using side information to reliably learn low-rank matrices from missing and corrupted obser- vations,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:15:36.013314Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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