ZenDNN v5.0: A Major Step Forward for Recommender Systems Researchers and Developers
On November 15, 2024, AMD released ZenDNN v5.0, the latest deep neural network library version. This update delivers hardware-specific optimizations, including support for Zen5 EPYC™...
Netflix’s Journey: Early Challenges and the Absence of Recommender Systems in Marc Randolph’s Vision
A recent 2-hour interview with Marc Randolph, the co-founder and first CEO of Netflix, provides a fascinating look into the early days of the company....
ACM UMAP 2025: (Preliminary) Call for Papers
UMAP (B ranked) released a preliminary Call for Papers for the UMAP 2025 conference in New York from June 16 – 20, 2025. We also...
RecSys Summer School 2025 in Vienna Announced: A Must-Attend for Students
Yesterday, at the closing session of the ACM Recommender Systems Conference in Bari, exciting news was shared. The RecSys Summer School 2025 will take place...
ACM RecSys 2025 to be held in Prague
The next edition of the ACM RecSys Conference will take place in Prague, Czech Republic, from September 22 to 26, 2025. ACM RecSys, an A-ranked...
Green Recommender-Systems at ACM RecSys 2024
There are only a few days left until the 18th ACM Conference on Recommender Systems begins, and one topic is quite prominently represented this year:...
Green Recommender Systems – A Call for Attention
Recommender systems are a cornerstone of many industries, from e-commerce to streaming services. Their role in shaping user choices is significant, driving customer engagement and...
CfP ACM TOIS: Special Section on Causality Representation Learning in LLMs-Driven Recommender Systems
The ACM Transactions on Information Systems (TOIS) announces a special section dedicated to “Causality Representation Learning in LLMs-Driven Recommender Systems.” This call for papers invites...
ECIR 2025 CfP published
The 47th European Conference on Information Retrieval (ECIR 2025) will take place from April 6 to April 10, 2025, in Lucca, Italy. ECIR is a...
How Meta trains large language models (LLM)
Adi Gangidi, KR Kishore, and Jenya Lee from Meta outline in the blog post “How Meta trains large language models at scale” Meta’s infrastructure advancements...