#recommenders — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #recommenders, aggregated by home.social.
-
📣 Want to curate future research in a #publishing system that ISN’T broken / exploitative / for-profit?
@pcirr is #RECRUITING #RECOMMENDERS (#editors) from ALL fields!👉 We seek post-PhD independent researchers w at least a handful of 1st author articles (to ensure experience w publishing)
➡️ We aim to have strong representation across topics, geographic regions, genders & in other areas where people have been TRADITIONALLY UNDERREPRESENTED in academia
ℹ️ Info: https://rr.peercommunityin.org/help/become_a_recommenders
-
📣 Want to curate future research in a #publishing system that ISN’T broken / exploitative / for-profit?
@pcirr is #RECRUITING #RECOMMENDERS (#editors) from ALL fields!👉 We seek post-PhD independent researchers w at least a handful of 1st author articles (to ensure experience w publishing)
➡️ We aim to have strong representation across topics, geographic regions, genders & in other areas where people have been TRADITIONALLY UNDERREPRESENTED in academia
ℹ️ Info: https://rr.peercommunityin.org/help/become_a_recommenders
-
In digital marketplaces, recommender engines are shifting from click-chasing to delivering real value, relying on stronger signals like trust, social networks, and sustained user satisfaction. Scaling meaningful engagement is difficult and frequently targeted by bad actors, so the true advantage lies in models that reward lasting value instead of fleeting attention. #Recommenders #LongTermValue #Trust #AI #ProductDesign
-
In digital marketplaces, recommender engines are shifting from click-chasing to delivering real value, relying on stronger signals like trust, social networks, and sustained user satisfaction. Scaling meaningful engagement is difficult and frequently targeted by bad actors, so the true advantage lies in models that reward lasting value instead of fleeting attention. #Recommenders #LongTermValue #Trust #AI #ProductDesign
-
Рекомендательная библиотека RePlay: сравнение с конкурентами RecBole и Recommenders на примере SOTA-модели SASRec
Привет, Хабр! Мы — команда ML‑разработчиков Сбера и Sber AI Lab. Хотим рассказать о нашем open‑source инструменте RePlay , который позволяет создавать рекомендательные системы с нуля, начиная с самых ранних DS‑экспериментов и заканчивая промышленной эксплуатацией. Статья будет интересна ML‑инженерам, разрабатывающим промышленные рекомендательные системы. Мотивацией для создания RePlay послужил тот факт, что все популярные на сегодняшний день RecSys‑фреймворки в основном нацелены на научные исследования и плохо оптимизированы для промышленной эксплуатации: не в состоянии обработать большой объём данных или требуют для этого значительных модификаций. Подробнее о создании библиотеки вы можете прочитать в соответствующей статье с RecSys 2024 . По той же ссылке вы найдёте обзорное видео о RePlay. Здесь же мы сравним RePlay с главными конкурентами — RecBole и Microsoft Recommenders. Разберём возможности, которые предоставляет каждая из библиотек, а затем, на примере SOTA‑модели, построим рекомендательную систему, начиная с ввода данных и заканчивая генерированием рекомендаций и подсчётом метрик. Сравним полученные модели по качеству и длительности обучения и инференса. В конце расскажем об уникальных возможностях RePlay, которые помогут ещё сильнее облегчить путь разработчика, по сравнению с использованием библиотек‑конкурентов
-
A HUGE thank you to our amazing @PeerCommunityIn Ecology & @pcirr #recommenders (Aurélie Coulon, @chrisdc77) & #reviewers: Gloriana Chaverri, Vedrana Šlipogor, @AVernouillet, @mdahirel, Andrea Griffin, Aliza le Roux, & 1 anonymous reviewer. Your PRE- and POST-study feedback was wonderful and helpful and really improved our research program! Thank you so much for investing your time and energy in this! #PeerReviewedPreregistration #Preprint
-
A HUGE thank you to our amazing @PeerCommunityIn Ecology & @pcirr #recommenders (Aurélie Coulon, @chrisdc77) & #reviewers: Gloriana Chaverri, Vedrana Šlipogor, @AVernouillet, @mdahirel, Andrea Griffin, Aliza le Roux, & 1 anonymous reviewer. Your PRE- and POST-study feedback was wonderful and helpful and really improved our research program! Thank you so much for investing your time and energy in this! #PeerReviewedPreregistration #Preprint
-
📖 #MondayMusings with Peer Community In 📖
It takes a #Community to build #OpenScience!
In this piece published in LSE Impact Blog, Per Pippin Aspaas suggests building diamond #OpenAccess models in the style of #Norwegian #Dugnad - a collective #social effort!
PCI thanks all its #authors, #reviewers, #recommenders, #managers, #partners & #funders for making it possible to go the #Dugnad way!! 🙏🙏 🙏
-
📖 #MondayMusings with Peer Community In 📖
It takes a #Community to build #OpenScience!
In this piece published in LSE Impact Blog, Per Pippin Aspaas suggests building diamond #OpenAccess models in the style of #Norwegian #Dugnad - a collective #social effort!
PCI thanks all its #authors, #reviewers, #recommenders, #managers, #partners & #funders for making it possible to go the #Dugnad way!! 🙏🙏 🙏
-
This 👇made my day! 🙏🙌
---
RT @j2bryson
We shouldn't assume that algorithms necessarily intermediate us. Early twitter and facebook, and current mastodon, allowed us to choose who we follow & boost, and therefore what information we're likely to see. #Internet4Trust #internetForTrust 1/2 #recommenders #AIEthics
https://twitter.com/j2bryson/status/1628315718868500482 -
This 👇made my day! 🙏🙌
---
RT @j2bryson
We shouldn't assume that algorithms necessarily intermediate us. Early twitter and facebook, and current mastodon, allowed us to choose who we follow & boost, and therefore what information we're likely to see. #Internet4Trust #internetForTrust 1/2 #recommenders #AIEthics
https://twitter.com/j2bryson/status/1628315718868500482 -
“What does a #datascientist in #Gaming do?”
1. #deeplearning
Used for so many things, from object detection to diffusion modeling for content creation.
2. #Reinforcementlearning
Obviously RL has been a huge focus for games like StarCraft, but all games can benefit from better AI or map playability.
3. #Recommenders
Recommenders can power matchmaking and in-game store fronts.
4. #Anomalydetection
Calling all #cybersecurity experts! Come work in gaming!