#recommendersystems — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #recommendersystems, aggregated by home.social.
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The Architecture Decisions CAIOs Cannot Delegate to Engineering
Architecture decisions, batch versus online prediction, cloud versus edge, offline versus online learning, coupled versus decoupled models. determine whether an AI system scales safely or collapses under real-world use. CAIOs and architects who get these trade-offs wrong don't get bad models; they get expensive rebuilds, stale predictions, or systems that optimize for outrage instead of value. -
The Architecture Decisions CAIOs Cannot Delegate to Engineering
Architecture decisions, batch versus online prediction, cloud versus edge, offline versus online learning, coupled versus decoupled models. determine whether an AI system scales safely or collapses under real-world use. CAIOs and architects who get these trade-offs wrong don't get bad models; they get expensive rebuilds, stale predictions, or systems that optimize for outrage instead of value. -
The Architecture Decisions CAIOs Cannot Delegate to Engineering
Architecture decisions, batch versus online prediction, cloud versus edge, offline versus online learning, coupled versus decoupled models. determine whether an AI system scales safely or collapses under real-world use. CAIOs and architects who get these trade-offs wrong don't get bad models; they get expensive rebuilds, stale predictions, or systems that optimize for outrage instead of value. -
The Architecture Decisions CAIOs Cannot Delegate to Engineering
Architecture decisions, batch versus online prediction, cloud versus edge, offline versus online learning, coupled versus decoupled models. determine whether an AI system scales safely or collapses under real-world use. CAIOs and architects who get these trade-offs wrong don't get bad models; they get expensive rebuilds, stale predictions, or systems that optimize for outrage instead of value. -
The Architecture Decisions CAIOs Cannot Delegate to Engineering
Architecture decisions, batch versus online prediction, cloud versus edge, offline versus online learning, coupled versus decoupled models. determine whether an AI system scales safely or collapses under real-world use. CAIOs and architects who get these trade-offs wrong don't get bad models; they get expensive rebuilds, stale predictions, or systems that optimize for outrage instead of value. -
Temperature Zero for Culture: Why Everything Is Starting to Look the Same
“Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want. If an algorithm feeds me Scandinavian crime dramas for ten years because I once watched two, my viewing history becomes airtight evidence that I love Scandinavian crime dramas.”
#AI #RecommenderSystems #LateStageCapitalism #Diversity
https://laurenleek.substack.com/p/temperature-zero-for-culture-why
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Temperature Zero for Culture: Why Everything Is Starting to Look the Same
“Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want. If an algorithm feeds me Scandinavian crime dramas for ten years because I once watched two, my viewing history becomes airtight evidence that I love Scandinavian crime dramas.”
#AI #RecommenderSystems #LateStageCapitalism #Diversity
https://laurenleek.substack.com/p/temperature-zero-for-culture-why
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Temperature Zero for Culture: Why Everything Is Starting to Look the Same
“Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want. If an algorithm feeds me Scandinavian crime dramas for ten years because I once watched two, my viewing history becomes airtight evidence that I love Scandinavian crime dramas.”
#AI #RecommenderSystems #LateStageCapitalism #Diversity
https://laurenleek.substack.com/p/temperature-zero-for-culture-why
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Temperature Zero for Culture: Why Everything Is Starting to Look the Same
“Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want. If an algorithm feeds me Scandinavian crime dramas for ten years because I once watched two, my viewing history becomes airtight evidence that I love Scandinavian crime dramas.”
#AI #RecommenderSystems #LateStageCapitalism #Diversity
https://laurenleek.substack.com/p/temperature-zero-for-culture-why
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Temperature Zero for Culture: Why Everything Is Starting to Look the Same
“Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want. If an algorithm feeds me Scandinavian crime dramas for ten years because I once watched two, my viewing history becomes airtight evidence that I love Scandinavian crime dramas.”
#AI #RecommenderSystems #LateStageCapitalism #Diversity
https://laurenleek.substack.com/p/temperature-zero-for-culture-why
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@petpet The general problem with most recommendation systems is that usually none of the algorithm's many parameters are in any way exposed to the user or opened up for customization. Most existing recommenders could be very easily tuned for different preferences & use cases, but instead they're consciously designed as black boxes and purely optimized for the preferences (and benefits) of the companies/platforms hosting the content, with a lot of these decisions driven by ad-tech as main business model...
From working with various UX teams over the years, it also has become absolutely clear to me that the default design behavior/goal is about patronizing/funneling people, treating everyone like toddlers with the omnipresent presumption that "less is more" in any situation, i.e. the main legacy of Steve Jobs & Apple's design philosophy which has been so influential in that field... That design approach equates "simplicity" with a literal lack of available options and then celebrates it as innovation. It's sickening and dehumanizing!
Another related post about this (from yesterday):
https://mastodon.thi.ng/@toxi/117115924105578188 -
@petpet The general problem with most recommendation systems is that usually none of the algorithm's many parameters are in any way exposed to the user or opened up for customization. Most existing recommenders could be very easily tuned for different preferences & use cases, but instead they're consciously designed as black boxes and purely optimized for the preferences (and benefits) of the companies/platforms hosting the content, with a lot of these decisions driven by ad-tech as main business model...
From working with various UX teams over the years, it also has become absolutely clear to me that the default design behavior/goal is about patronizing/funneling people, treating everyone like toddlers with the omnipresent presumption that "less is more" in any situation, i.e. the main legacy of Steve Jobs & Apple's design philosophy which has been so influential in that field... That design approach equates "simplicity" with a literal lack of available options and then celebrates it as innovation. It's sickening and dehumanizing!
Another related post about this (from yesterday):
https://mastodon.thi.ng/@toxi/117115924105578188 -
@petpet The general problem with most recommendation systems is that usually none of the algorithm's many parameters are in any way exposed to the user or opened up for customization. Most existing recommenders could be very easily tuned for different preferences & use cases, but instead they're consciously designed as black boxes and purely optimized for the preferences (and benefits) of the companies/platforms hosting the content, with a lot of these decisions driven by ad-tech as main business model...
From working with various UX teams over the years, it also has become absolutely clear to me that the default design behavior/goal is about patronizing/funneling people, treating everyone like toddlers with the omnipresent presumption that "less is more" in any situation, i.e. the main legacy of Steve Jobs & Apple's design philosophy which has been so influential in that field... That design approach equates "simplicity" with a literal lack of available options and then celebrates it as innovation. It's sickening and dehumanizing!
Another related post about this (from yesterday):
https://mastodon.thi.ng/@toxi/117115924105578188 -
@petpet The general problem with most recommendation systems is that usually none of the algorithm's many parameters are in any way exposed to the user or opened up for customization. Most existing recommenders could be very easily tuned for different preferences & use cases, but instead they're consciously designed as black boxes and purely optimized for the preferences (and benefits) of the companies/platforms hosting the content, with a lot of these decisions driven by ad-tech as main business model...
From working with various UX teams over the years, it also has become absolutely clear to me that the default design behavior/goal is about patronizing/funneling people, treating everyone like toddlers with the omnipresent presumption that "less is more" in any situation, i.e. the main legacy of Steve Jobs & Apple's design philosophy which has been so influential in that field... That design approach equates "simplicity" with a literal lack of available options and then celebrates it as innovation. It's sickening and dehumanizing!
Another related post about this (from yesterday):
https://mastodon.thi.ng/@toxi/117115924105578188 -
@petpet The general problem with most recommendation systems is that usually none of the algorithm's many parameters are in any way exposed to the user or opened up for customization. Most existing recommenders could be very easily tuned for different preferences & use cases, but instead they're consciously designed as black boxes and purely optimized for the preferences (and benefits) of the companies/platforms hosting the content, with a lot of these decisions driven by ad-tech as main business model...
From working with various UX teams over the years, it also has become absolutely clear to me that the default design behavior/goal is about patronizing/funneling people, treating everyone like toddlers with the omnipresent presumption that "less is more" in any situation, i.e. the main legacy of Steve Jobs & Apple's design philosophy which has been so influential in that field... That design approach equates "simplicity" with a literal lack of available options and then celebrates it as innovation. It's sickening and dehumanizing!
Another related post about this (from yesterday):
https://mastodon.thi.ng/@toxi/117115924105578188 -
Learn how AI recommendation engines power Spotify, Amazon, YouTube, and more without relying on magic or black-box mystique. https://hackernoon.com/how-recommender-systems-actually-work #recommendersystems
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Learn how AI recommendation engines power Spotify, Amazon, YouTube, and more without relying on magic or black-box mystique. https://hackernoon.com/how-recommender-systems-actually-work #recommendersystems
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Learn how AI recommendation engines power Spotify, Amazon, YouTube, and more without relying on magic or black-box mystique. https://hackernoon.com/how-recommender-systems-actually-work #recommendersystems
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Learn how AI recommendation engines power Spotify, Amazon, YouTube, and more without relying on magic or black-box mystique. https://hackernoon.com/how-recommender-systems-actually-work #recommendersystems
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Learn how AI recommendation engines power Spotify, Amazon, YouTube, and more without relying on magic or black-box mystique. https://hackernoon.com/how-recommender-systems-actually-work #recommendersystems
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RE: https://curia.social-network.europa.eu/@Curia/116758852644484436
Well. A lot of digital rights lawyers seem very excited about this ruling from the Court of Justice today - in particular, the final paragraph 122:
⚡️a website that controls what viewers see via an algorithm is liable for the content⚡️
Full judgment (in French): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:62024CJ0188
Case refs:
C-188/24 WebGroup Czech Republic and NKL Associates
and
C-190/24 Coyote System
#DigitalRights #bigtech #algorithms #InfiniteScroll #SurveillanceCapitalism #RecommenderSystems #EU #AI #Law
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RE: https://curia.social-network.europa.eu/@Curia/116758852644484436
Well. A lot of digital rights lawyers seem very excited about this ruling from the Court of Justice today - in particular, the final paragraph 122:
⚡️a website that controls what viewers see via an algorithm is liable for the content⚡️
Full judgment (in French): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:62024CJ0188
Case refs:
C-188/24 WebGroup Czech Republic and NKL Associates
and
C-190/24 Coyote System
#DigitalRights #bigtech #algorithms #InfiniteScroll #SurveillanceCapitalism #RecommenderSystems #EU #AI #Law
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RE: https://curia.social-network.europa.eu/@Curia/116758852644484436
Well. A lot of digital rights lawyers seem very excited about this ruling from the Court of Justice today - in particular, the final paragraph 122:
⚡️a website that controls what viewers see via an algorithm is liable for the content⚡️
Full judgment (in French): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:62024CJ0188
Case refs:
C-188/24 WebGroup Czech Republic and NKL Associates
and
C-190/24 Coyote System
#DigitalRights #bigtech #algorithms #InfiniteScroll #SurveillanceCapitalism #RecommenderSystems #EU #AI #Law
-
RE: https://curia.social-network.europa.eu/@Curia/116758852644484436
Well. A lot of digital rights lawyers seem very excited about this ruling from the Court of Justice today - in particular, the final paragraph 122:
⚡️a website that controls what viewers see via an algorithm is liable for the content⚡️
Full judgment (in French): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:62024CJ0188
Case refs:
C-188/24 WebGroup Czech Republic and NKL Associates
and
C-190/24 Coyote System
#DigitalRights #bigtech #algorithms #InfiniteScroll #SurveillanceCapitalism #RecommenderSystems #EU #AI #Law
-
RE: https://curia.social-network.europa.eu/@Curia/116758852644484436
Well. A lot of digital rights lawyers seem very excited about this ruling from the Court of Justice today - in particular, the final paragraph 122:
⚡️a website that controls what viewers see via an algorithm is liable for the content⚡️
Full judgment (in French): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:62024CJ0188
Case refs:
C-188/24 WebGroup Czech Republic and NKL Associates
and
C-190/24 Coyote System
#DigitalRights #bigtech #algorithms #InfiniteScroll #SurveillanceCapitalism #RecommenderSystems #EU #AI #Law
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Noch eine Woche bis zum Bewerbungsschluss:
Projektkoordinator:in Gutachter:innen-Empfehlungssystem für Zeitschriften (m/w/d), 24 Monate, 75% E13, ab 1.10.
Aufstockungsoptionen prüfen wir gerne. Das wird ein spannendes Projekt - gerne anschauen, weiterleiten. Stehe für Nachfragen zur Verfügung.
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Noch eine Woche bis zum Bewerbungsschluss:
Projektkoordinator:in Gutachter:innen-Empfehlungssystem für Zeitschriften (m/w/d), 24 Monate, 75% E13, ab 1.10.
Aufstockungsoptionen prüfen wir gerne. Das wird ein spannendes Projekt - gerne anschauen, weiterleiten. Stehe für Nachfragen zur Verfügung.
-
Noch eine Woche bis zum Bewerbungsschluss:
Projektkoordinator:in Gutachter:innen-Empfehlungssystem für Zeitschriften (m/w/d), 24 Monate, 75% E13, ab 1.10.
Aufstockungsoptionen prüfen wir gerne. Das wird ein spannendes Projekt - gerne anschauen, weiterleiten. Stehe für Nachfragen zur Verfügung.
-
Noch eine Woche bis zum Bewerbungsschluss:
Projektkoordinator:in Gutachter:innen-Empfehlungssystem für Zeitschriften (m/w/d), 24 Monate, 75% E13, ab 1.10.
Aufstockungsoptionen prüfen wir gerne. Das wird ein spannendes Projekt - gerne anschauen, weiterleiten. Stehe für Nachfragen zur Verfügung.
-
Noch eine Woche bis zum Bewerbungsschluss:
Projektkoordinator:in Gutachter:innen-Empfehlungssystem für Zeitschriften (m/w/d), 24 Monate, 75% E13, ab 1.10.
Aufstockungsoptionen prüfen wir gerne. Das wird ein spannendes Projekt - gerne anschauen, weiterleiten. Stehe für Nachfragen zur Verfügung.
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A Job I Like or a Job I Can Get: Designing Job #RecommenderSystems Using Field Experiments https://d.repec.org/n?u=RePEc:arx:papers:2603.21699&r=&r=exp
"… welfare-optimal RSs rank vacancies by an expected-surplus index, and shows why rankings based solely on utility, #hiring probabilities, or observed application behavior are generically suboptimal
… Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark.While the joint application-and-hiring probability is not welfare-optimal in theory, it emerges as a strong empirical benchmark in our setting. This result is structural rather than algorithmic: application probabilities are empirically small and remain so even under recommendation rules designed to stimulate applications
… rankings based solely on application behavior are theoretically fragile
… Machine-learning tools can substantially improve matching outcomes, but only when embedded in a framework that defines the economic objective and disciplines behavioral assumptions with experimental evidence. Without such a framework, RSs optimized for observable behaviors may perform well on predictive metrics yet remain misaligned with welfare-relevant outcomes."
#LaborMarkets #jobtech #socialWelfare #ExperimentalEcon -
A Job I Like or a Job I Can Get: Designing Job #RecommenderSystems Using Field Experiments https://d.repec.org/n?u=RePEc:arx:papers:2603.21699&r=&r=exp
"… welfare-optimal RSs rank vacancies by an expected-surplus index, and shows why rankings based solely on utility, #hiring probabilities, or observed application behavior are generically suboptimal
… Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark.While the joint application-and-hiring probability is not welfare-optimal in theory, it emerges as a strong empirical benchmark in our setting. This result is structural rather than algorithmic: application probabilities are empirically small and remain so even under recommendation rules designed to stimulate applications
… rankings based solely on application behavior are theoretically fragile
… Machine-learning tools can substantially improve matching outcomes, but only when embedded in a framework that defines the economic objective and disciplines behavioral assumptions with experimental evidence. Without such a framework, RSs optimized for observable behaviors may perform well on predictive metrics yet remain misaligned with welfare-relevant outcomes."
#LaborMarkets #jobtech #socialWelfare #ExperimentalEcon -
A Job I Like or a Job I Can Get: Designing Job #RecommenderSystems Using Field Experiments https://d.repec.org/n?u=RePEc:arx:papers:2603.21699&r=&r=exp
"… welfare-optimal RSs rank vacancies by an expected-surplus index, and shows why rankings based solely on utility, #hiring probabilities, or observed application behavior are generically suboptimal
… Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark.While the joint application-and-hiring probability is not welfare-optimal in theory, it emerges as a strong empirical benchmark in our setting. This result is structural rather than algorithmic: application probabilities are empirically small and remain so even under recommendation rules designed to stimulate applications
… rankings based solely on application behavior are theoretically fragile
… Machine-learning tools can substantially improve matching outcomes, but only when embedded in a framework that defines the economic objective and disciplines behavioral assumptions with experimental evidence. Without such a framework, RSs optimized for observable behaviors may perform well on predictive metrics yet remain misaligned with welfare-relevant outcomes."
#LaborMarkets #jobtech #socialWelfare #ExperimentalEcon -
A Job I Like or a Job I Can Get: Designing Job #RecommenderSystems Using Field Experiments https://d.repec.org/n?u=RePEc:arx:papers:2603.21699&r=&r=exp
"… welfare-optimal RSs rank vacancies by an expected-surplus index, and shows why rankings based solely on utility, #hiring probabilities, or observed application behavior are generically suboptimal
… Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark.While the joint application-and-hiring probability is not welfare-optimal in theory, it emerges as a strong empirical benchmark in our setting. This result is structural rather than algorithmic: application probabilities are empirically small and remain so even under recommendation rules designed to stimulate applications
… rankings based solely on application behavior are theoretically fragile
… Machine-learning tools can substantially improve matching outcomes, but only when embedded in a framework that defines the economic objective and disciplines behavioral assumptions with experimental evidence. Without such a framework, RSs optimized for observable behaviors may perform well on predictive metrics yet remain misaligned with welfare-relevant outcomes."
#LaborMarkets #jobtech #socialWelfare #ExperimentalEcon -
A Job I Like or a Job I Can Get: Designing Job #RecommenderSystems Using Field Experiments https://d.repec.org/n?u=RePEc:arx:papers:2603.21699&r=&r=exp
"… welfare-optimal RSs rank vacancies by an expected-surplus index, and shows why rankings based solely on utility, #hiring probabilities, or observed application behavior are generically suboptimal
… Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark.While the joint application-and-hiring probability is not welfare-optimal in theory, it emerges as a strong empirical benchmark in our setting. This result is structural rather than algorithmic: application probabilities are empirically small and remain so even under recommendation rules designed to stimulate applications
… rankings based solely on application behavior are theoretically fragile
… Machine-learning tools can substantially improve matching outcomes, but only when embedded in a framework that defines the economic objective and disciplines behavioral assumptions with experimental evidence. Without such a framework, RSs optimized for observable behaviors may perform well on predictive metrics yet remain misaligned with welfare-relevant outcomes."
#LaborMarkets #jobtech #socialWelfare #ExperimentalEcon -
📢 Fantastic news from the Digital Science Center! 📢
The open‑access paper “Maximal Transparency for Online Recommender Systems” is out in Philosophy & Technology. A truly interdisciplinary effort across philosophy, bioinformatics, mathematics, computer science, and law.
Read the full article here: https://link.springer.com/article/10.1007/s13347-026-01084-7
#RecommenderSystems #Transparency #AIEthics #OpenAccess #InterdisciplinaryResearch #Philosophy #Technology #EUAIAct
1/5 -
📢 Fantastic news from the Digital Science Center! 📢
The open‑access paper “Maximal Transparency for Online Recommender Systems” is out in Philosophy & Technology. A truly interdisciplinary effort across philosophy, bioinformatics, mathematics, computer science, and law.
Read the full article here: https://link.springer.com/article/10.1007/s13347-026-01084-7
#RecommenderSystems #Transparency #AIEthics #OpenAccess #InterdisciplinaryResearch #Philosophy #Technology #EUAIAct
1/5 -
📢 Fantastic news from the Digital Science Center! 📢
The open‑access paper “Maximal Transparency for Online Recommender Systems” is out in Philosophy & Technology. A truly interdisciplinary effort across philosophy, bioinformatics, mathematics, computer science, and law.
Read the full article here: https://link.springer.com/article/10.1007/s13347-026-01084-7
#RecommenderSystems #Transparency #AIEthics #OpenAccess #InterdisciplinaryResearch #Philosophy #Technology #EUAIAct
1/5 -
📢 Fantastic news from the Digital Science Center! 📢
The open‑access paper “Maximal Transparency for Online Recommender Systems” is out in Philosophy & Technology. A truly interdisciplinary effort across philosophy, bioinformatics, mathematics, computer science, and law.
Read the full article here: https://link.springer.com/article/10.1007/s13347-026-01084-7
#RecommenderSystems #Transparency #AIEthics #OpenAccess #InterdisciplinaryResearch #Philosophy #Technology #EUAIAct
1/5 -
📢 Fantastic news from the Digital Science Center! 📢
The open‑access paper “Maximal Transparency for Online Recommender Systems” is out in Philosophy & Technology. A truly interdisciplinary effort across philosophy, bioinformatics, mathematics, computer science, and law.
Read the full article here: https://link.springer.com/article/10.1007/s13347-026-01084-7
#RecommenderSystems #Transparency #AIEthics #OpenAccess #InterdisciplinaryResearch #Philosophy #Technology #EUAIAct
1/5 -
Two teams from LIPN will present their joined work at IPMU 2026 👏.
Congratulations to Amal Beldi and Louenas Bounia for their work on Uncertainty-Aware Contextual Recommendation under Possible Worlds Semantics!
This paper proposes a probabilistic framework for uncertainty-aware contextual recommendation grounded in probabilistic database semantics.
#LIPN #RecommenderSystems #DecisionMaking -
Two teams from LIPN will present their joined work at IPMU 2026 👏.
Congratulations to Amal Beldi and Louenas Bounia for their work on Uncertainty-Aware Contextual Recommendation under Possible Worlds Semantics!
This paper proposes a probabilistic framework for uncertainty-aware contextual recommendation grounded in probabilistic database semantics.
#LIPN #RecommenderSystems #DecisionMaking -
Two teams from LIPN will present their joined work at IPMU 2026 👏.
Congratulations to Amal Beldi and Louenas Bounia for their work on Uncertainty-Aware Contextual Recommendation under Possible Worlds Semantics!
This paper proposes a probabilistic framework for uncertainty-aware contextual recommendation grounded in probabilistic database semantics.
#LIPN #RecommenderSystems #DecisionMaking -
Two teams from LIPN will present their joined work at IPMU 2026 👏.
Congratulations to Amal Beldi and Louenas Bounia for their work on Uncertainty-Aware Contextual Recommendation under Possible Worlds Semantics!
This paper proposes a probabilistic framework for uncertainty-aware contextual recommendation grounded in probabilistic database semantics.
#LIPN #RecommenderSystems #DecisionMaking -
Future recommendation infrastructures must integrate evaluation protocols, fairness metrics, and reproducible pipelines as first-class design principles—not afterthoughts.
The paper “WarpRec” proposes a framework that unifies academic rigor with industrial-scale recommendation systems, aiming for responsibility, reproducibility, and efficiency at scale.
https://arxiv.org/abs/2602.17442v1
#RecommenderSystems #ResponsibleAI #MachineLearning -
Future recommendation infrastructures must integrate evaluation protocols, fairness metrics, and reproducible pipelines as first-class design principles—not afterthoughts.
The paper “WarpRec” proposes a framework that unifies academic rigor with industrial-scale recommendation systems, aiming for responsibility, reproducibility, and efficiency at scale.
https://arxiv.org/abs/2602.17442v1
#RecommenderSystems #ResponsibleAI #MachineLearning -
Future recommendation infrastructures must integrate evaluation protocols, fairness metrics, and reproducible pipelines as first-class design principles—not afterthoughts.
The paper “WarpRec” proposes a framework that unifies academic rigor with industrial-scale recommendation systems, aiming for responsibility, reproducibility, and efficiency at scale.
https://arxiv.org/abs/2602.17442v1
#RecommenderSystems #ResponsibleAI #MachineLearning -
Future recommendation infrastructures must integrate evaluation protocols, fairness metrics, and reproducible pipelines as first-class design principles—not afterthoughts.
The paper “WarpRec” proposes a framework that unifies academic rigor with industrial-scale recommendation systems, aiming for responsibility, reproducibility, and efficiency at scale.
https://arxiv.org/abs/2602.17442v1
#RecommenderSystems #ResponsibleAI #MachineLearning -
Future recommendation infrastructures must integrate evaluation protocols, fairness metrics, and reproducible pipelines as first-class design principles—not afterthoughts.
The paper “WarpRec” proposes a framework that unifies academic rigor with industrial-scale recommendation systems, aiming for responsibility, reproducibility, and efficiency at scale.
https://arxiv.org/abs/2602.17442v1
#RecommenderSystems #ResponsibleAI #MachineLearning -
Paige Saunders @paige (rather than Dawn Walker @dawn as I erroneously wrote earlier!) has this great video, 'We Have An Algorithm Problem' at https://video.fedihost.co/w/a1522517-704e-44a3-aa0e-5c4e7d49e7d1, where he says that fediverse users' suspicion of algorithms is completely warranted, but now that we have been able to opt out of algorithmic content where we had zero agency, we need a conversation about what it means to opt in with algorithms under our own control.
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Paige Saunders @paige (rather than Dawn Walker @dawn as I erroneously wrote earlier!) has this great video, 'We Have An Algorithm Problem' at https://video.fedihost.co/w/a1522517-704e-44a3-aa0e-5c4e7d49e7d1, where he says that fediverse users' suspicion of algorithms is completely warranted, but now that we have been able to opt out of algorithmic content where we had zero agency, we need a conversation about what it means to opt in with algorithms under our own control.
-
Paige Saunders @paige (rather than Dawn Walker @dawn as I erroneously wrote earlier!) has this great video, 'We Have An Algorithm Problem' at https://video.fedihost.co/w/a1522517-704e-44a3-aa0e-5c4e7d49e7d1, where he says that fediverse users' suspicion of algorithms is completely warranted, but now that we have been able to opt out of algorithmic content where we had zero agency, we need a conversation about what it means to opt in with algorithms under our own control.
-
Dawn Walker @dawn has this great video, 'We Have An Algorithm Problem' at https://video.fedihost.co/w/a1522517-704e-44a3-aa0e-5c4e7d49e7d1, where he says that fediverse users' suspicion of algorithms is completely warranted, but now that we have been able to opt out of algorithmic content where we had zero agency, we need a conversation about what it means to opt in with algorithms under our own control.
-
Paige Saunders @paige (rather than Dawn Walker @dawn as I erroneously wrote earlier!) has this great video, 'We Have An Algorithm Problem' at https://video.fedihost.co/w/a1522517-704e-44a3-aa0e-5c4e7d49e7d1, where he says that fediverse users' suspicion of algorithms is completely warranted, but now that we have been able to opt out of algorithmic content where we had zero agency, we need a conversation about what it means to opt in with algorithms under our own control.
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"Banning #socialmedia for young people will ignore the incredibly harmful societal effects of modern social media for most of the population…
The most immediate solution is to ban companies from using #recommendersystems entirely (outside a few specific cases); that would restore our freedom to choose what we see online, and at least pause our descent into the years-long spiral towards increased extremism, misinformation, social media addiction & polarisation."
https://www.thejournal.ie/readme/opinion-social-media-ban-australia-go-further-recommender-systems-algorithm-6901718-Dec2025/ -
"Banning #socialmedia for young people will ignore the incredibly harmful societal effects of modern social media for most of the population…
The most immediate solution is to ban companies from using #recommendersystems entirely (outside a few specific cases); that would restore our freedom to choose what we see online, and at least pause our descent into the years-long spiral towards increased extremism, misinformation, social media addiction & polarisation."
https://www.thejournal.ie/readme/opinion-social-media-ban-australia-go-further-recommender-systems-algorithm-6901718-Dec2025/ -
"Banning #socialmedia for young people will ignore the incredibly harmful societal effects of modern social media for most of the population…
The most immediate solution is to ban companies from using #recommendersystems entirely (outside a few specific cases); that would restore our freedom to choose what we see online, and at least pause our descent into the years-long spiral towards increased extremism, misinformation, social media addiction & polarisation."
https://www.thejournal.ie/readme/opinion-social-media-ban-australia-go-further-recommender-systems-algorithm-6901718-Dec2025/ -
"Banning #socialmedia for young people will ignore the incredibly harmful societal effects of modern social media for most of the population…
The most immediate solution is to ban companies from using #recommendersystems entirely (outside a few specific cases); that would restore our freedom to choose what we see online, and at least pause our descent into the years-long spiral towards increased extremism, misinformation, social media addiction & polarisation."
https://www.thejournal.ie/readme/opinion-social-media-ban-australia-go-further-recommender-systems-algorithm-6901718-Dec2025/ -
"Banning #socialmedia for young people will ignore the incredibly harmful societal effects of modern social media for most of the population…
The most immediate solution is to ban companies from using #recommendersystems entirely (outside a few specific cases); that would restore our freedom to choose what we see online, and at least pause our descent into the years-long spiral towards increased extremism, misinformation, social media addiction & polarisation."
https://www.thejournal.ie/readme/opinion-social-media-ban-australia-go-further-recommender-systems-algorithm-6901718-Dec2025/ -
I love it when recommender systems are so chronically off that it just confirms the coming automated dystopian future we have built will be 90% Brazil and 10% LOTF.
#recommendersystems #researchgate #academia #academicchatter