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- Title
Forgetting techniques for stream-based matrix factorization in recommender systems.
- Authors
Matuszyk, Pawel; Vinagre, João; Spiliopoulou, Myra; Jorge, Alípio Mário; Gama, João
- Abstract
Forgetting is often considered a malfunction of intelligent agents; however, in a changing world forgetting has an essential advantage. It provides means of adaptation to changes by removing effects of obsolete (not necessarily old) information from models. This also applies to intelligent systems, such as recommender systems, which learn users’ preferences and predict future items of interest. In this work, we present unsupervised forgetting techniques that make recommender systems adapt to changes of users’ preferences over time. We propose eleven techniques that select obsolete information and three algorithms that enforce the forgetting in different ways. In our evaluation on real-world datasets, we show that forgetting obsolete information <bold>significantly</bold> improves predictive power of recommender systems.
- Subjects
RECOMMENDER systems; INTELLIGENT agents; FACTORIZATION; MATRICES (Mathematics); DATA mining
- Publication
Knowledge & Information Systems, 2018, Vol 55, Issue 2, p275
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-017-1091-8