Kuo L.-YChou C.-KMING-SYAN CHEN2021-09-022021-09-02202110414347https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097731388&doi=10.1109%2fTKDE.2019.2924894&partnerID=40&md5=a103d06d1fd092e37c4cf17fad9df37bhttps://scholars.lib.ntu.edu.tw/handle/123456789/581061Matrix factorization (MF) has earned great success on recommender systems. However, the common-used regression-based MF not only is sensitive to outliers but also unable to guarantee that the predicted values are in line with the user preference orders, which is the basis of common measures of recommender systems, e.g., nDCG. To overcome the aforementioned drawbacks, we propose a framework for personalized ranking of Poisson factorization that utilizes learning-to-rank based posteriori instead of the classical regression-based ones. Owing to the combination, the proposed framework not only preserves user preference but also performs well on a sparse matrix. Since the posteriori that combines learning to rank and Poisson factorization does not follow the conjugate prior relationship, we estimate variational parameters approximately and propose two optimization approaches based on variational inference. As long as the used learning-to-rank model has the 1st and 2nd order partial derivatives, by exploiting our framework, the proposed optimizing algorithm can maximize the posteriori whichever the used learning-to-rank model is. In the experiment, we show that the proposed framework outperforms the state-of-the-art methods and achieves promising results on consuming log and rating datasets for multiple recommendation tasks. ? 1989-2012 IEEE.Factorization; Matrix algebra; Recommender systems; Matrix factorizations; Optimization approach; Optimizing algorithm; Partial derivatives; Preference order; State-of-the-art methods; Variational inference; Variational parameters; Learning to rankjournal article10.1109/TKDE.2019.29248942-s2.0-85097731388