May 13, 2018 · We propose a probabilistic matrix tri-factorization model that incorporates both user and product preferences. By taking both types of ...
Nov 21, 2024 · Learning Dual Preferences with Non-negative Matrix Tri-Factorization for Top-N Recommender System. May 2018; Lecture Notes in Computer Science.
Learning Dual Preferences with Non-negative Matrix Tri ...
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In this paper, we propose a user-product topic model to capture both user preferences and attractive characteristics of products. Different from conventional ...
Experiments on two real-world data sets validate the effectiveness of our method in Top-N recommendations. Original language, English. Title of host publication ...
Dive into the research topics of 'Learning dual preferences with non-negative Matrix tri-factorization for Top-N recommender system'. Together they form a ...
Title: Learning dual preferences with non-negative matrix tri-factorization for Top-N recommender system. Author(s):, Xie, Haoran · Wang, Philips Fu Lee.
May 21, 2018 · Experiments on two real-world data sets validate the effectiveness of our method in Top-N recommendations. Research Area(s). Matrix tri- ...
In recommender systems, personal characteristic is possessed by not only users but also displaying products. Users have their personal rating patterns while ...
Learning Dual Preferences with Non-negative Matrix Tri-Factorization for Top-N Recommender System · Xiangsheng LiYanghui Rao +4 authors. Jian Yin. Computer ...
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