Recommender systems: the case of repeated interaction in matrix factorization
(2017) International Conference on Web Intelligence — Location: Leipzig, Germany (23.August.2017)
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- Abstract
- 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.
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Sommer, F., Lecron, F., & Fouss, F. (2017). Recommender systems: the case of repeated interaction in matrix factorization. WI′17 Proceedings of the International Conference on Web Intelligence, p. 843-847. https://doi.org/10.1145/3106426.3106522