Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison | Paper Citation Record

Observation 2fa7b6ef-3ef6-43ef-ae4d-8e3f993b6d66 · outbound

This paper cites Enhancing quality of experience in telecommunica- tion networks: A review of frameworks and machine learning algorithms,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Enhancing quality of experience in telecommunica- tion networks: A review of frameworks and machine learning algorithms,

Reference 1

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Observation 8678919e-b67f-4d5d-adac-4fad9ac7b000 · outbound

This paper cites Survey on quality of experience evalua- tion for cloud-based interactive applications,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Survey on quality of experience evalua- tion for cloud-based interactive applications,

Reference 2

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Observation 10c3cac8-b3b5-49ba-8ea2-cbb9023bbccf · outbound

This paper cites Investigation into user-centric qoe and network-centric parameters for youtube service on mobile networks,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Investigation into user-centric qoe and network-centric parameters for youtube service on mobile networks,

Reference 3

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This paper cites Qoe in multimedia domain: a user-centric quality assess- ment,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Qoe in multimedia domain: a user-centric quality assess- ment,

Reference 4

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Observation bb2d4a02-449f-459c-94be-f612417732b2 · outbound

This paper cites Acqua: A user friendly platform for lightweight net- work monitoring and qoe forecasting,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Acqua: A user friendly platform for lightweight net- work monitoring and qoe forecasting,

Reference 5

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Observation 0d6b8c1c-3b1e-420d-852a-7f00b274ce50 · outbound

This paper cites Enhanc- ing qoe assessment in fwa: Leveraging network kpis and user feedback analysis,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Enhanc- ing qoe assessment in fwa: Leveraging network kpis and user feedback analysis,

Reference 6

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Observation 26ecd71d-61a6-4fd3-8af7-c2b4b3db9226 · outbound

This paper cites Ucwe: A user-centric approach for web quality of experience measurement,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Ucwe: A user-centric approach for web quality of experience measurement,

Reference 7

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Observation 5ed299f1-bb96-4ff4-b826-beec0195d207 · outbound

This paper cites On user-centric modular qoe pre- diction for voip based on machine-learning algo- rithms,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison On user-centric modular qoe pre- diction for voip based on machine-learning algo- rithms,

Reference 8

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This paper cites An efficient network-based qoe assess- ment framework for multimedia networks using a machine learning approach,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison An efficient network-based qoe assess- ment framework for multimedia networks using a machine learning approach,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:47.357445Z

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This paper cites Quality of experience estimation of en- crypted video streaming by using machine learning methods,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Quality of experience estimation of en- crypted video streaming by using machine learning methods,

Reference 10

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Observation c555c0f3-2549-42fd-86bd-8d8d7652a748 · outbound

This paper cites A survey on multimedia services qoe assessment and machine learning-based prediction,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison A survey on multimedia services qoe assessment and machine learning-based prediction,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:47.308971Z

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Observation 7aa0c29c-8643-45b7-a2cc-0850b4e90746 · outbound

This paper cites Speech and multimedia Transmission Quality (STQ); Quality of Experience; A Monitoring Archi- tecture,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Speech and multimedia Transmission Quality (STQ); Quality of Experience; A Monitoring Archi- tecture,

Reference 12

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This paper cites Survey and challenges of qoe management issues in wireless net- works,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Survey and challenges of qoe management issues in wireless net- works,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:47.255551Z

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This paper cites Qoe-aware analysis and management of multimedia services in 5g and be- yond heterogeneous networks,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Qoe-aware analysis and management of multimedia services in 5g and be- yond heterogeneous networks,

Reference 14

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Observation cd86a43b-c6dc-4144-a7e6-91fbeaa4d34e · outbound

This paper cites Qoe modeling for http adaptive video streaming–a survey and open challenges,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Qoe modeling for http adaptive video streaming–a survey and open challenges,

Reference 15

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This paper cites Cache-enabled adaptive video streaming: A qoe-based evaluation study,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Cache-enabled adaptive video streaming: A qoe-based evaluation study,

Reference 16

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Observation 1facd777-fb2f-4ae7-8b47-e19504b6f5b3 · outbound

This paper cites Qoe management of multimedia streaming services in future networks: A tutorial and survey,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Qoe management of multimedia streaming services in future networks: A tutorial and survey,

Reference 17

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Observation a3c9ba11-5537-4183-9e76-2e650690d096 · outbound

This paper cites Machine learning based quality of experience (qoe) prediction approach in enterprise multimedia networks,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Machine learning based quality of experience (qoe) prediction approach in enterprise multimedia networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:47.137855Z

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This paper cites Llm4band: Enhancing reinforcement learning with large language models for accurate bandwidth estimation,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Llm4band: Enhancing reinforcement learning with large language models for accurate bandwidth estimation,

Reference 19

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This paper cites REAL-TIME MON- ITORING AND ENHANCEMENT OF MULTI- USER COMMUNICATIONS WITH AI AGENTS,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison REAL-TIME MON- ITORING AND ENHANCEMENT OF MULTI- USER COMMUNICATIONS WITH AI AGENTS,

Reference 20

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Observation 337eb601-3ddb-4dfd-b41b-bfd87bd4ea34 · outbound

This paper cites Quality-of-service aware llm serving.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Quality-of-service aware llm serving

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:47.089889Z

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This paper cites Large Language Model Aided QoS Prediction for Service Recommendation.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Large Language Model Aided QoS Prediction for Service Recommendation

Reference 22

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This paper cites Quality measurement of screen images via foreground perception and background suppression,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Quality measurement of screen images via foreground perception and background suppression,

Reference 23

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This paper cites Fine- grained region adaptive loop filter for super-block video coding,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Fine- grained region adaptive loop filter for super-block video coding,

Reference 24

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This paper cites Quality of experience pre- diction model for progressive downloading over mo- bile broadcast networks,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Quality of experience pre- diction model for progressive downloading over mo- bile broadcast networks,

Reference 25

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This paper cites Mmlscu: A dataset for multi-modal multi-domain live streaming comment understanding,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Mmlscu: A dataset for multi-modal multi-domain live streaming comment understanding,

Reference 26

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Time- sync comments analyzation for understanding sub- scribers to live streaming services,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:46.883554Z

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This paper cites Enhanc- ing multimodal affective analysis with learned live comment features,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Enhanc- ing multimodal affective analysis with learned live comment features,

Reference 28

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Of- fensive language detection on video live streaming chat,

Reference 29

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Twitchchat: A dataset for exploring livestream chat,

Reference 30

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Twitch.tv Chat Log Data,

Reference 31

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Seman- tic collaborative filtering recommender system using cnns,

Reference 32

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This paper cites Conex based qoe feedback to enhance qoe,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Conex based qoe feedback to enhance qoe,

Reference 33

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Document-level text simplification,

Reference 34

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Performance of 4 pre-trained sentence transformer models in the semantic query of a systematic review dataset on peri-implantitis,

Reference 35

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Enabling auditory large language models for automatic speech quality evaluation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:45.328821Z

Source-reported events for the cited work

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This paper cites Extraction of subjective information from large language models,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Extraction of subjective information from large language models,

Reference 37

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This paper cites The past, present and better future of feedback learning in large language models for subjective hu- man preferences and values,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison The past, present and better future of feedback learning in large language models for subjective hu- man preferences and values,

Reference 38

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This paper cites A Survey of Uncertainty Estimation Methods on Large Language Models.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison A Survey of Uncertainty Estimation Methods on Large Language Models

Reference 39

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Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Language models are few-shot learners,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:44.811610Z

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This paper cites Gpt-4o overview and capabilities,.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Gpt-4o overview and capabilities,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:44.615820Z

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This paper cites Available: https://www.mdpi.com/ 2078-2489/15/2/68.

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison Available: https://www.mdpi.com/ 2078-2489/15/2/68

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:00:45.464569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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