Top Tweets for #RECSYS
I conceptualize the User–Item Interaction Lifecycle as comprising three stages, each of which entails distinct user costs that vary significantly across domains. Following this framework, I consider different #RecSys tasks in relation to the stages of the lifecycle they address.

This reminds me of some of the research in #RecSys
There is a serious problem in academic research today.
PhD students/researchers are publishing a good number of papers, sometimes even in very good journals without having a basic understanding of the methodology behind their own work.
They collect data, put it into Spss, R, Stata or another software…click a few options and then simply report whatever numbers the software gives them…This is particularly concerning when such work gets published in good journals.
There is a major difference between running an analysis and understanding an analysis.
Knowing how to operate statistical software is not the same as knowing statistics, just as knowing how to operate a microscope is not the same as understanding biology.
May good research sense prevail
Recommendation latest research progress | 10. Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 9. ERASE: EaRly bAckpropagation SchEdule for Faster Training of Modern Recommendation Systems
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 8. Leaf Values as Coordinates: Exact Contrastive Explanation for Gradient-Boosted Ensembles
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 7. A Privacy Budgeting Framework for Online Experimentation
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 6. The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 5. OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Ranking
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 4. SCoRD: Semantic-Assisted Continual Retriever-Reranker Distillation for LLM-Based Recommendation
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 3. Training-Free LLM-Based Recommendation with Post-LLM Item Refinement Using Collaborative Signals
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 2. PILOT Technical Report
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

Recommendation latest research progress | 1. Do Sequential Recommendation Benchmarks Really Require Higher-Order Sequence Modelling?
#AI #DeepLearning #ArtificialInteligence #Datascience #Research #recsys

A Comprehensive Tutorial on #RecSys Engines and Implementation! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/s2Wxk9cPdp

Delighted to see my #RecSys paper featured in Advances in Computing by ACM!
When Science Goes Agentic https://t.co/dmwnCgGVBU
So glad to learn that we both share the very same vision for the future of #RecSys!
Both position papers focus on how AI agents change who we are recommending to. The Agentic Web is here, and RecSys needs a paradigm shift!

I'll be giving an invited talk on our work on Multimodal LLMs with Xiaohongshu at the DaQuaMRec workshop at #RecSys2026 on September 28 in Minneapolis, MN! 🇺🇸
Many thanks to the organizers.
Looking forward to it!
@ACMRecSys @sjtu1896 @xiaohongshu @DaQuaMRec #RecSys #LLM #MLLM #MultimodalLLM #Recommendation

Solving-Cold-Start Conundrum in #RecSys! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/d2EPhmrlta

While #SIGIR2026 is underway, I'm happy to see that our short paper, published six years ago, has now reached 100 citations on Google Scholar.
Looking at recent #RecSys research, I see a healthy shift: beyond proposing new algorithms, the community is increasingly re-examining the foundations—baselines, reproducibility, datasets, evaluation, the academia–industry gap, and the assumptions behind our benchmarks.
The next challenge may be even bigger.
As AI agents increasingly act on behalf of users, we need to rethink not just recommender algorithms, but the entire RecSys ecosystem. If AI agents generate a significant share of internet traffic, recommendation, search, and their business models will all need to be reimagined.
That transformation is already underway with the rise of agentic search (e.g., @octenai) in the search domain, and RecSys is likely to follow. In the future, recommender systems may not only be built by AI agents but increasingly consumed by AI agents acting on behalf of users.

Dear #recsys #recsys2026 community, my team at Penguin Random House 📚 (one of the biggest book platforms) is hiring a Staff ML Scientist – Personalization. If interested, plz reach out to me or the hiring manager K.T. Chang (https://t.co/uAfgBLnjTU)
job:https://t.co/U5fLiGHop2
TorchRec (Beta Release) for #RecSys. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/6jcydUySS4

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