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In this work, we propose FM-Pair, an adaptation of Factorization Machines with a pairwise loss function, making them effective for datasets with implicit feedback.
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Improving Pairwise Learning for Item Recommendation from Implicit Feedback. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining
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Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild
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Personalized recommendation via cross-domain triadic factorization. In Proceedings of the 22nd international conference on World Wide Web
Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, and Can Zhu. 2013 · 2013
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Mining Contextual Movie Similarity with Matrix Factorization for Context-aware Recommendation
Yue Shi, Martha Larson, and Alan Hanjalic. 2013 · 2013
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A recommendation model based on collaborative filtering and factorization machines for social networks. In Broadband Network Multimedia Technology (IC-BNMT), 2013 5th IEEE International Conference on
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WrapRec: An Easy Extension of Recommender System Libraries. In Proceedings of 8th ACM International Conference of Recommender Systems
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Linas Baltrunas, Karen Church, Alexandros Karatzoglou, and Nuria Oliver. 2015 · 2015
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Cross-Domain Recommender Systems
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Recommendation with the Right Slice: Speeding Up Collaborative Filtering with Factorization Machines.. In RecSys Posters
Babak Loni, Martha Larson, Alexandros Karatzoglou, and Alan Hanjalic. 2015 · 2015
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Personalized ranking with pairwise Factorization Machines
Weiyu Guo, Shu Wu, Liang Wang, and Tieniu Tan. 2016 · 2016
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Field-aware Factorization Machines for CTR Prediction. In Proceedings of the 10th ACM Conference on Recommender Systems
Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016 · 2016
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