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- W2743689810 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." @default.
- W2743689810 created "2017-08-17" @default.
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- W2743689810 date "2017-08-23" @default.
- W2743689810 modified "2023-09-26" @default.
- W2743689810 title "Recommender systems" @default.
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- W2743689810 doi "https://doi.org/10.1145/3106426.3106522" @default.
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