New research shows a tuned recommendation engine can boost click‑through rates by 10% while cutting inference cost. The paper dives into model‑serving tricks, optimization for large language models, and deployment efficiency for production AI. Open‑source practitioners will love the practical benchmarks. #RecommendationEngine #InferenceOptimization #ModelServing #ClickThroughRate

🔗 https://aidailypost.com/news/recommendation-engine-lifts-click-through-10-efficiency-needed

Data Annotation for Smarter AI & Recommendations in E-commerce

Discover how quality data annotation empowers e-commerce businesses to train smarter AI systems and recommendation engines. From image and text labeling to scalable automation, learn how accurate datasets drive personalization, better search, and higher engagement in online retail.

#DataAnnotation #EcommerceAI #RecommendationEngine #AIDrivenRetail #MachineLearning #DataLabeling #PersonalizedShopping

It isn't clear to me whether an LLM is replacing Twitter's current recommendation-engine (i.e., what some misleadingly call "an algorithm" or "the algorithm") with an LLM.

Or, if the LLM is more of a front-end to a non-LLM based recommendation-engine.

RE: https://twitter.com/elonmusk/status/1979217645854511402

#Grok #LLM #RecommendationEngine #Twitter

RE: https://mastodon.social/@cheeaun/115381698564197327

This is an argument for (also) having a recommendation-engine based feed.

(I.e., what some mistakenly call "the algorithm" or an "algorithmic feed".)

“I know the comparison here is 450 followers to 4,500, but follower number isn't indicative of the full picture. Think about TikTok, and how you can have 50 followers but have posts that reach millions of views.”

RE: https://cara.app/post/2e0d29d3-a57a-4b70-b0d0-9cefc15176ca

#FediUX #Fediverse #FediverseUX #RecommendationEngine

Unlock growth with #MachineLearning Recommendation Systems! Learn 4 strategies using Generative AI to personalize customer experiences. #AI #RecommendationEngine #Personalization

https://teguhteja.id/machine-learning-recommendation-systems/?utm_source=mastodon&utm_medium=jetpack_social

Bài viết về các hệ thống gợi ý sản phẩm (recommendation engines) trong thương mại điện tử, dành cho kỹ sư và kiến trúc sư.

- Các thuật toán cơ bản: Content-Based Filtering, Collaborative Filtering và các biến thể kết hợp.
- Sự phát triển của deep learning trong lĩnh vực này: MF -> NCF, two-tower retrieval + ANN, sequential models (Transformers), graph models (GNNs), LLM + multimodal, AutoML.

#AI #ecommerce #recommendationengine #trituenhao #thươngmạđiệntử #gợiy

https://www.reddit.com/r/progra

AI recommendation engines often fail due to poor data quality, lack of personalization, and algorithmic biases. Learn how to build smarter AI-driven recommendation systems with advanced machine learning techniques and optimized data strategies. Improve accuracy, relevance, and user engagement.

Read more at https://www.amplework.com/blog/why-ai-recommendation-engines-fail-how-to-build-better/

#ai #recommendationengine #machinelearning #artificialintelligence