A survey of active learning in collaborative filtering recommender systems

DOI10.1016/J.COSREV.2016.05.002zbMath1371.68232OpenAlexW2441269247WikidataQ61440721 ScholiaQ61440721MaRDI QIDQ739010

D. Kharzeev

Publication date: 16 August 2016

Published in: Computer Science Review (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cosrev.2016.05.002

zbMATH Keywords

active learningcollaborative filteringrecommender systemspreference elicitationnew itemcold startnew userrating elicitation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35) Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.) (68U35) Research exposition (monographs, survey articles) pertaining to computer science (68-02)

Related Items (4)

Partial data querying through racing algorithms ⋮ Recommender Systems: A Review ⋮ A new collaborative filtering recommendation method based on transductive SVM and active learning ⋮ Active matrix factorization for surveys

Uses Software

• PRMLT

Cites Work

• Active Learning
• Machine Learning
• Why Does Collaborative Filtering Work? Transaction-Based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: A survey of active learning in collaborative filtering recommender systems