Machine learning-based personalization helps individual users get improved customer experiences. Find out how recommender systems work in this article.
⚠️ [ARCHIVED] This version has been archived as of october 2024 and will not be updated anymore, please refer to the README for a link to the new version. This is the official repository for the Recommender Systems course at Politecnico di Milano.
This repository provides a curated list of papers and tutorials about Recommender Systems (RS) including systematic tutorials, comprehensive surveys, general recommender system, social recommender system, deep learing-based recommender system, cold start problem in recommender system, efficient recommender ...
Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection ...doi:10.1016/j.eswa.2009.07.008Heng-Li YangChen-Shu WangExpert Systems with Applications...
Networking Overview DPUs and SuperNICs Ethernet InfiniBand GPUs GeForce NVIDIA RTX / Quadro Data Center Embedded Systems Jetson DRIVE AGX Clara AGX Application Frameworks AI Inference - Triton Automotive - DRIVE Cloud-AI Video Streaming - Maxine Computational Lithography - cuLitho Cyber...
To address these objectives, we develop a recommender system model for drug sensitivity prediction, called Drug Efficacy Estimation Recommender System (DEERS). DEERS incorporates two autoencoders to project the drug and cell line features, respectively, into lower dimensional representations, and uses a...
recommender systems as well as the pros and cons of the currently available classifications. We have created a classification of recommender techniques, including various user inputs, knowledge from the database, the ways in which the recommendation will be presented to the user and the technologies...
Buffalo is a fast and scalable production-ready open source project for recommender systems. Buffalo effectively utilizes system resources, enabling high performance even on low-spec machines. The implementation is optimized for CPU and SSD. Even so, it shows good performance with GPU accelerator, to...
Our recommender, like other systems, requires data to perform properly. Several prepared commands can be used to generate such data: flask seed seed - it allows to seed a database with any number of synthetic users and services. The exact number can be adjusted here seed, flask seed seed_...
Health recommender systems ML: Machine learning AA: Active aging DL: Deep-learning AI: Artificial intelligence CF: Collaborative filtering PH: Physical CB: Content-based filtering PE: Personal HR: Hybrid recommendation PT: Persuasion technologies ...