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Recommender systems: the case of repeated interaction in matrix factorization

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Sommer, Félix [UCL] Lecron, Fabian [Université de Mons, Belgium ] Fouss, François [UCL]

This work presents a new matrix factorization recommender system approach, that takes repeated interaction into account. We analyze if and how users' repeated interaction behavior---such as repeat purchases---can be integrated into a recommender system. We develop a method that takes advantage of this additional data dimension that is studied in many other fields to derive useful conclusions. Furthermore, we empirically test our method on real-life retailer data and on the Last.fm dataset. We compare our algorithm with popular matrix factorization approaches. Results indicate that our method manages to slightly outperform the existing methods.

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Publication date 2017
Language Anglais
Conference "International Conference on Web Intelligence ", Leipzig, Germany (du 23/08/2017 au 26/05/2018)
Peer reviewed yes
Host document "WI'17 Proceedings of the International Conference on Web Intelligence"- p. 843-847 (ISBN : 978-1-4503-4951-2)
Publisher ACM (New York)
Publication status Publié
Affiliations UCL - SSH/LouRIM - Louvain Research Institute in Management and Organizations
UCL - SST/ICTM/INGI - Pôle en ingénierie informatique
Links
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Bibliographic reference Sommer, Félix ; Lecron, Fabian ; Fouss, François. Recommender systems: the case of repeated interaction in matrix factorization.International Conference on Web Intelligence (Leipzig, Germany , du 23/08/2017 au 26/05/2018). In: WI'17 Proceedings of the International Conference on Web Intelligence, ACM : New York2017, p. 843-847
Permanent URL http://hdl.handle.net/2078.1/197520