Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks | Paper Citation Record

Observation 0df3d952-d0f0-4189-b107-47003f320b3a · outbound

This paper cites GPT-4 Technical Report.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks GPT-4 Technical Report

Reference 1

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Observation a0cf46a8-a787-4212-8500-0bb13834b40e · outbound

This paper cites Adomavicius and A.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Adomavicius and A

Reference 2

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Observation 3b542a2c-b7b4-4fd0-918e-49fc566b1fb5 · outbound

This paper cites Auto-surprise: An automated recommender-system (autorecsys) library with tree of parzens estimator (tpe) optimization.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Auto-surprise: An automated recommender-system (autorecsys) library with tree of parzens estimator (tpe) optimization

Reference 3

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Observation 2da8ae15-3cbe-4cd4-829d-7d52871140d2 · outbound

This paper cites Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation

Reference 4

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Observation dcfd9e41-b664-4ed3-9a68-67462c59e9dc · outbound

This paper cites Exploiting graph structured cross-domain representation for multi-domain recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Exploiting graph structured cross-domain representation for multi-domain recommendation

Reference 5

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Observation 38056e1e-97d8-430b-8db1-da5974a2de4b · outbound

This paper cites Fab: content-based, collaborative recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Fab: content-based, collaborative recommendation

Reference 6

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Observation d3ac1039-04fa-4990-b0f3-f9f1be8fbfe7 · outbound

This paper cites Tallrec: An effective and efficient tuning framework to align large language model with recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Tallrec: An effective and efficient tuning framework to align large language model with recommendation

Reference 7

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Observation 6bdff0e3-dfb7-4da7-a2b4-9cbe16e86876 · outbound

This paper cites Hyperopt: a python library for model selection and hyperparameter optimization.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Hyperopt: a python library for model selection and hyperparameter optimization

Reference 8

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Observation e01bce2b-8c77-4169-ba61-c15d6a23444c · outbound

This paper cites Language models are few-shot learners.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Language models are few-shot learners

Reference 9

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Observation d6e030f4-d666-4c6b-902a-af7ec24eecd6 · outbound

This paper cites Hybrid recommender systems: Survey and experiments.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Hybrid recommender systems: Survey and experiments

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.571857Z

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Observation b576e7e5-5315-42ff-908c-e84cc2e404b1 · outbound

This paper cites A system- atic study on reproducibility of reinforcement learning in recommendation systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks A system- atic study on reproducibility of reinforcement learning in recommendation systems

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.561732Z

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Observation 0f07467b-ef4f-4503-966a-5c4dbc79870d · outbound

This paper cites Pefa: Parameter-free adapters for large-scale embedding-based retrieval models.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Pefa: Parameter-free adapters for large-scale embedding-based retrieval models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.550695Z

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Observation 30245745-5153-412f-82f6-53795af6fc3e · outbound

This paper cites A comprehensive survey on automated machine learning for recommendations.ACM Transactions on Recommender Systems, 2(2):1–38, 2024.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks A comprehensive survey on automated machine learning for recommendations.ACM Transactions on Recommender Systems, 2(2):1–38, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.539850Z

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Observation 398a7e7d-621d-4e06-978b-c343792fbea5 · outbound

This paper cites Neural feature search: A neural architecture for automated feature engineering.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Neural feature search: A neural architecture for automated feature engineering

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.529016Z

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Observation 8d076621-2d6d-473c-91d8-b421c0f2a089 · outbound

This paper cites Uncovering chatgpt’s capabilities in recommender systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Uncovering chatgpt’s capabilities in recommender systems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.517367Z

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Observation 55b5ba47-8e6e-425c-ba88-38dca20d8e0c · outbound

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

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.507315Z

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Observation 4b057978-6dd7-4fdf-af51-c57bd43b1e34 · outbound

This paper cites The Llama 3 Herd of Models.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks The Llama 3 Herd of Models

Reference 17

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Observation 0048cc5f-a6f7-4768-a821-7aef91f8bcc2 · outbound

This paper cites Lenskit: a modular recommender framework.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Lenskit: a modular recommender framework

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.496407Z

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Observation c41b97ef-58e4-4515-905e-af59968aca5c · outbound

This paper cites Neural architecture search: A survey.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Neural architecture search: A survey

Reference 19

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Observation 1f9d501e-3a9a-48e0-bd01-052c4125802e · outbound

This paper cites Are we really making much progress? a worrying analysis of recent neural recommendation approaches.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Are we really making much progress? a worrying analysis of recent neural recommendation approaches

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.481360Z

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Observation 353b20d2-b510-46de-a365-0b7daa90edaf · outbound

This paper cites Cross- domain meta-learner for cold-start recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Cross- domain meta-learner for cold-start recommendation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.471570Z

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Observation 23963dcb-5a96-4b8d-9288-8dc0a3a3b277 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 22

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Observation 910c0d4d-1179-44fd-8825-7aaba4a9a0ca · outbound

This paper cites The effect of third party implementations on reproducibil- ity.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks The effect of third party implementations on reproducibil- ity

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.461827Z

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

Observation 4a3428a4-df4c-4e7f-bf17-5cf07ae2eb1e · outbound

This paper cites The autofeat python library for automated feature engineering and selection.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks The autofeat python library for automated feature engineering and selection

Reference 24

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Observation 6bcd4ac3-0599-4400-879c-4299cb9768e8 · outbound

This paper cites Ecat: A entire space continual and adaptive transfer learning framework for cross-domain recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Ecat: A entire space continual and adaptive transfer learning framework for cross-domain recommendation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.445047Z

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Observation fd300cc2-8ef8-4932-8654-1ec90c708ec9 · outbound

This paper cites Collaborative filtering for implicit feedback datasets.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Collaborative filtering for implicit feedback datasets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.434831Z

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Observation 2a197e12-717c-4a26-be74-1ab298e9c36b · outbound

This paper cites Self-supervised contrastive enhancement with symmetric few-shot learning towers for cold-start news recom- mendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Self-supervised contrastive enhancement with symmetric few-shot learning towers for cold-start news recom- mendation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.424535Z

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Observation f4ea4bb4-62e2-40f6-a7e8-76cb402f20fe · outbound

This paper cites Knowledge- aware cross-semantic alignment for domain-level zero-shot recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Knowledge- aware cross-semantic alignment for domain-level zero-shot recommendation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.414558Z

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Observation 69095090-d28b-4a40-8ddd-79ba1e0e3af5 · outbound

This paper cites Automatic multi-task learning framework with neural architecture search in recommendations.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Automatic multi-task learning framework with neural architecture search in recommendations

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.404810Z

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Observation 3ae2b6e3-c129-4504-b05c-68faa87bee2f · outbound

This paper cites Neural input search for large scale recommendation models.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Neural input search for large scale recommendation models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.394677Z

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 b12af8cf-03a2-4c33-a906-50d9f60e1edf · outbound

This paper cites Large language models meet collaborative filtering: An efficient all-round llm-based recommender system.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Large language models meet collaborative filtering: An efficient all-round llm-based recommender system

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.384906Z

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Observation 19ba1f51-0091-46da-b20c-97ef920404b9 · outbound

This paper cites Large language models are zero-shot reasoners.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Large language models are zero-shot reasoners

Reference 32

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Observation 061d01bc-90b3-4d83-b700-6216f9681502 · outbound

This paper cites Matrix factorization techniques for recom- mender systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Matrix factorization techniques for recom- mender systems

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:48.612901Z

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Observation 521c50d4-e4ff-4fdc-be4f-325c29979bd5 · outbound

This paper cites Advances in collaborative filtering.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Advances in collaborative filtering

Reference 34

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Observation ee2e084a-093f-4bb8-9800-9150482326e2 · outbound

This paper cites Auto- weka 2.0: Automatic model selection and hyperparameter optimization in weka.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Auto- weka 2.0: Automatic model selection and hyperparameter optimization in weka

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.356052Z

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Observation 7018908d-bc6b-487d-a4a0-bae9593bab18 · outbound

This paper cites Melu: Meta-learned user preference estimator for cold-start recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Melu: Meta-learned user preference estimator for cold-start recommendation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.343928Z

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Observation 9b7464c9-19b1-4d1a-b679-0b233b5859eb · outbound

This paper cites Prompt distillation for efficient llm-based recommenda- tion.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Prompt distillation for efficient llm-based recommenda- tion

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.333579Z

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Observation eb8a6554-6bcb-467c-ae9e-6d0fff25d438 · outbound

This paper cites Automlp: Automated mlp for sequential recommendations.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Automlp: Automated mlp for sequential recommendations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.322500Z

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 6863f256-6b3e-46de-88fc-c28b0b200d47 · outbound

This paper cites Recai: Leveraging large language models for next-generation recommender systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Recai: Leveraging large language models for next-generation recommender systems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.311706Z

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Observation ade655fe-7e2b-4492-b2db-e76869c5a827 · outbound

This paper cites Llara: Large language-recommendation assistant.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Llara: Large language-recommendation assistant

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:48.634784Z

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Observation ac5f61e5-d2d5-40f9-9937-727b463e0534 · outbound

This paper cites Tune: A Research Platform for Distributed Model Selection and Training.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Tune: A Research Platform for Distributed Model Selection and Training

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:48.637568Z

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Unavailable: canonical work link unavailable.

Observation 6aabdc36-1855-4e69-9c02-072a2d3d89c8 · outbound

This paper cites DARTS: differentiable architecture search.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks DARTS: differentiable architecture search

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.294840Z

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 f8f380dc-b79a-458b-a046-5abbbd16b559 · outbound

This paper cites Automated feature selection: A reinforcement learning perspective.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Automated feature selection: A reinforcement learning perspective

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.283829Z

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 df4a8145-a5c0-4806-a212-2c8219917b1b · outbound

This paper cites Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.272895Z

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 c3f584da-0e05-4d58-8670-cb7116074327 · outbound

This paper cites Content-based recommender systems: State of the art and trends.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Content-based recommender systems: State of the art and trends

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.262516Z

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 2e94b7e5-faf1-42d5-a475-521f7e0f6a0e · outbound

This paper cites Bayesian optimization for automated model selection.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Bayesian optimization for automated model selection

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.249681Z

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 d218ca4d-89c9-4864-848b-7a85833374d3 · outbound

This paper cites Recpack: An (other) experimentation toolkit for top-n recommendation using implicit feedback data.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Recpack: An (other) experimentation toolkit for top-n recommendation using implicit feedback data

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.237442Z

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 a582ce71-64fb-4b85-a09b-f135a05d6cc3 · outbound

This paper cites Ctr-bert: Cost-effective knowledge distillation for billion-parameter teacher models.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Ctr-bert: Cost-effective knowledge distillation for billion-parameter teacher models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.225512Z

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 2774ad75-79cd-4405-a30e-3659b9a82607 · outbound

This paper cites The elephant in the room: Rethinking the usage of pre-trained language model in sequential recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks The elephant in the room: Rethinking the usage of pre-trained language model in sequential recommendation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.214549Z

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 e534c5be-93d3-4387-a4e6-7fe8c4c08a20 · outbound

This paper cites Cornac: A comparative framework for multimodal recommender systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Cornac: A comparative framework for multimodal recommender systems

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.203743Z

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 8071370a-768c-4706-a27c-18fce4a52d54 · outbound

This paper cites Item-based collaborative filtering recommendation algorithms.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Item-based collaborative filtering recommendation algorithms

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:48.669534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d26e15c7-ea7a-4f00-9555-12a288c0bb6b · outbound

This paper cites Selecting a classification method by cross-validation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Selecting a classification method by cross-validation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.185810Z

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 09d220b3-464e-47b0-bf6b-8a87108b1ecc · outbound

This paper cites Autorec: Autoencoders meet collaborative filtering.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Autorec: Autoencoders meet collaborative filtering

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.174285Z

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 76c94cca-c0bf-4fd7-94ff-e0423454e88e · outbound

This paper cites RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:09:48.851235Z

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 9b2c0a11-bc50-4cc6-b334-a80512cf4ae6 · outbound

This paper cites Librec-auto: A tool for recommender systems experimen- tation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Librec-auto: A tool for recommender systems experimen- tation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.163532Z

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 b961286c-dfaf-4c2a-b68a-4e33579e4ecf · outbound

This paper cites Large language models enhanced collaborative filtering.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Large language models enhanced collaborative filtering

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.152023Z

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 2dfb6037-9cfe-496e-971d-ec0a5bb84799 · outbound

This paper cites Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.140821Z

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 4ea85dd0-d06f-46f1-b54e-bc0ee4d052dc · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Gemini: A Family of Highly Capable Multimodal Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:48.692397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb09d921-51dc-49b5-8829-db608c7cc53a · outbound

This paper cites A meta-learning perspective on cold-start recommendations for items.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks A meta-learning perspective on cold-start recommendations for items

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.130264Z

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 0a0fd474-9348-4687-92c9-817a84139846 · outbound

This paper cites Introducing lenskit-auto, an experimental au- tomated recommender system (autorecsys) toolkit.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Introducing lenskit-auto, an experimental au- tomated recommender system (autorecsys) toolkit

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.120185Z

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 54c48c9f-a7e3-4b07-8c09-afa69050dfa7 · outbound

This paper cites Autosr: Automatic sequen- tial recommendation system design.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Autosr: Automatic sequen- tial recommendation system design

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.110187Z

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 d21e3eb6-5569-48ba-b9a0-7ea26cc2395a · outbound

This paper cites Sta: Self-controlled text augmentation for improving text classifications.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Sta: Self-controlled text augmentation for improving text classifications

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.100000Z

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 303398da-76da-43df-8b84-55d4397402fc · outbound

This paper cites A pre-trained zero-shot sequential recom- mendation framework via popularity dynamics.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks A pre-trained zero-shot sequential recom- mendation framework via popularity dynamics

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.088696Z

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 ea171600-fac6-4afb-90c3-4782f592a24d · outbound

This paper cites Au- torec: An automated recommender system.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Au- torec: An automated recommender system

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.077871Z

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 4e254681-6616-490b-b943-523f7cb50b32 · outbound

This paper cites Automatic fea- ture selection by one-shot neural architecture search in recommendation systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Automatic fea- ture selection by one-shot neural architecture search in recommendation systems

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.066753Z

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 e751e354-6945-4c4d-943b-7eb4decb9f15 · outbound

This paper cites Llmrec: Large language models with graph augmentation for recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Llmrec: Large language models with graph augmentation for recommendation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:48.717025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b41f30fc-f34c-42c4-bc3e-f6d992b755b6 · outbound

This paper cites Reproduce, replicate, reevaluate.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Reproduce, replicate, reevaluate

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.049445Z

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 9f48a966-bf78-42fd-9306-42de1f6e31e1 · outbound

This paper cites Loureiro.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Loureiro

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.038856Z

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 a16fc283-198b-4d2c-b1c0-d65648bf2911 · outbound

This paper cites Dataset-Agnostic Recommender Systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Dataset-Agnostic Recommender Systems

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:09:48.825889Z

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 dce9ce48-d00a-4d76-8e49-37e461bb4542 · outbound

This paper cites Empowering news recommendation with pre-trained language models.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Empowering news recommendation with pre-trained language models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.028456Z

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 e76d7bf6-7f60-4981-be33-3536bb01a07a · outbound

This paper cites MM-GEF: Multi-modal representation meet collaborative filtering.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks MM-GEF: Multi-modal representation meet collaborative filtering

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:09:48.808828Z

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 d671de3f-8d6e-477d-b78d-8bdd0d7d5292 · outbound

This paper cites M2eu: Meta learning for cold-start recommendation via enhancing user preference estimation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks M2eu: Meta learning for cold-start recommendation via enhancing user preference estimation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:49.016111Z

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 86545577-8ca1-4cc8-896f-50824f3366bd · outbound

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

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Towards open-world recommendation with knowledge augmentation from large language models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:48.740083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dd99aff-a756-41ca-b9e9-57841b519268 · outbound

This paper cites Extreme meta-classification for large- scale zero-shot retrieval.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Extreme meta-classification for large- scale zero-shot retrieval

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.997712Z

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 797861bc-023f-4267-8862-9c6715696cef · outbound

This paper cites On hyperparameter optimization of machine learning algorithms: Theory and practice.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks On hyperparameter optimization of machine learning algorithms: Theory and practice

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:48.747287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 195751e1-f819-4884-9e55-a88fea185048 · outbound

This paper cites ihas: Instance-wise hierarchical architecture search for deep learning recommendation models.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks ihas: Instance-wise hierarchical architecture search for deep learning recommendation models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.979456Z

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 23d39771-a4de-4b8f-9658-9d14cb3244e7 · outbound

This paper cites Dns-rec: Data-aware neural architecture search for recommender systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Dns-rec: Data-aware neural architecture search for recommender systems

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.969094Z

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 dff1bf39-f2f1-49aa-84c8-176c21a65e97 · outbound

This paper cites A collaborative transfer learning framework for cross-domain recommendation.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks A collaborative transfer learning framework for cross-domain recommendation

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.958611Z

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 3b5d6648-4111-468b-abb2-fa794c1d53f8 · outbound

This paper cites Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.947673Z

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 4798de8c-d982-4866-984b-88ae6cb49b6d · outbound

This paper cites Automl for deep recommender systems: A survey.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Automl for deep recommender systems: A survey

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.937548Z

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 802591a1-efce-4f21-bbbc-b97c3fd6e2e2 · outbound

This paper cites Nas-ctr: efficient neural architecture search for click-through rate prediction.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Nas-ctr: efficient neural architecture search for click-through rate prediction

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.927891Z

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 cfddf425-a891-4dea-b05b-ba7501760032 · outbound

This paper cites Difer: Differentiable automated feature engineering.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Difer: Differentiable automated feature engineering

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.918424Z

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 d322fba5-68e9-4c54-8fb2-04a953a66651 · outbound

This paper cites Bars: Towards open benchmarking for recommender systems.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Bars: Towards open benchmarking for recommender systems

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:48.908805Z

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 90e83a9f-7e79-4c15-8d9a-257ef39e0506 · outbound

This paper cites an unresolved cited work.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:09:48.898756Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers