Forgetting techniques for stream-based matrix factorization in recommender systems

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Matuszyk, P (Author) Other; Vinagre, J (Author) Other; Spiliopoulou, M (Author) Other; Jorge, AM (Author) FCUP; João Gama (Author) FEP

Journal

Title: Knowledge and Information Systems

Vol. 55

Pages: 275-304

ISSN: 0219-1377

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 17 Citations

Scopus - 21 Citations

Other information

Authenticus ID: P-00N-R6Y

DOI: 10.1007/s10115-017-1091-8

Abstract (EN): 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 significantly improves predictive power of recommender systems.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 30