Everything about Recommender system
System to predict users' preferences
A recommender system, also called a recommendation engine or content discovery platform is a type of information filtering system that aims to suggest items most relevant for some input or to a particular user. In the context of social media, search engines, and other online services, a recommender system for a given service might sometimes informally but erroneously be referred to as the service's "algorithm". The use of recommender systems is pervasive, with commonly recognised examples including the generation of playlists for video and music streaming services, product recommendations for e-commerce platforms, and topics and individual items posted on social media platforms and the open web. Online services employ machine learning or specialized deep learning recommendation models (DLRMs) that analyze user behavior and preferences to generate personalized content feeds for millions of users at any moment. The value of these systems becomes particularly evident in scenarios where users must select from a large number of options, such as products, media, or content. Suggestions are typically designed to shorten or work as a substitute for decision-making processes, including the selection of a product, musical selection, or online news source to read. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Recommender systemA 5-minute orientation
- Skim a recent paper: Graph Neural Networks in Recommender Systems: A SurveyInfluential work since 2022
- Pick up a classic book: Recommender Systems HandbookThe most-reprinted book on the subject
- Read the landmark paper: Matrix Factorization Techniques for Recommender SystemsCited 9,471 times
- Go sideways: Collaborative filteringA closely related topic
- Go sideways: Cold start (recommender systems)A closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Matrix Factorization Techniques for Recommender Systems
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
The Netflix Recommender System
A Multimedia Recommender System
An adaptive doctor-recommender system
Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm
A Hybrid Recommender System Based on User-Recommender Interaction
Distributionally-Informed Recommender System Evaluation
🆕 Influential since 2022
Graph Neural Networks in Recommender Systems: A Survey
A Survey on Knowledge Graph-Based Recommender Systems
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
A systematic review and research perspective on recommender systems
A review of battery energy storage systems and advanced battery management system for different applications: Challenges and recommendations
📚 Books worth reading
Recommender Systems HandbookFrancesco Ricci · 2010
Statistical Methods for Recommender SystemsDeepak K. Agarwal, Bee-Chung Chen · 2015
Netflix RecommendsMattias Frey · 2021- 📕Product recommendations in e-commerce retailing applicationsNicolas Knotzer · 2008
Recommender SystemsCharu C. Aggarwal · 2016- 📕Transport of dangerous goodsUnited Nations. Committee of Experts on the Transport of Dangerous Goods · 1956
Trust For Intelligent RecommendationTouhid Bhuiyan · 2013- 📕Report of the Task Force on the Shuttle-Mir Rendezvous & Docking MissionsNASA Advisory Council. Task Force on the Shuttle-Mir Rendezvous & Docking Missions. · 1994
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Collaborative filtering Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems. Collaborative filtering has…
- Cold start (recommender systems) Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling. Specifically, it concerns…
- Matrix factorization (recommender systems) Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing…
- Algorithmic curation Algorithm curation is the selection of online media by technologies such as recommender systems and personalized search. Curation entails the selective…
- Gravity R&D Gravity R&D (full name: Gravity Research & Development Zrt.) is an IT vendor specialized in recommender systems. Gravity was founded by members of the…
- Learning to rank Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning…
- Joseph A. Konstan Joseph A. Konstan is an American computer scientist and professor at the University of Minnesota. His research focuses on human–computer interaction, recommende…
- GroupLens Research 44°58′27″N 93°13′57″W / 44.974280°N 93.232502°W / 44.974280; -93.232502 GroupLens Research is a human–computer interaction research lab in the Department…