M
Ming-Feng Tsai
Researcher at National Chengchi University
Publications - Â 111
Citations - Â 4352
Ming-Feng Tsai is an academic researcher from National Chengchi University. The author has contributed to research in topics: Ranking (information retrieval) & Recommender system. The author has an hindex of 21, co-authored 90 publications receiving 3501 citations. Previous affiliations of Ming-Feng Tsai include University of Missouri & National Taiwan University.
Papers
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Proceedings ArticleDOI
Learning to rank: from pairwise approach to listwise approach
TL;DR: It is proposed that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning, and introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning.
Proceedings ArticleDOI
FRank: a ranking method with fidelity loss
TL;DR: An algorithm named FRank is proposed based on a generalized additive model for the sake of minimizing the fedelity loss and learning an effective ranking function and the experimental results show that the proposed algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search.
Proceedings ArticleDOI
HOP-rec: high-order proximity for implicit recommendation
TL;DR: This paper presents HOP-Rec, a unified and efficient method that incorporates factorization and graph-based models and significantly outperforms the state of the art on a range of large-scale real-world datasets.
Journal ArticleDOI
Dual functions of a small regulatory subunit in the mitochondrial calcium uniporter complex
Ming-Feng Tsai,Charles B Phillips,Matthew J. Ranaghan,Chen-Wei Tsai,Yujiao Wu,Carole Willliams,Christopher Miller +6 more
TL;DR: A second function of EMRE is revealed: to maintain tight MICU regulation of the MCU pore, a role that requires EMRE to bind MICU1 using its conserved C-terminal polyaspartate tail, ensuring that all transport-competent uniporters are tightly regulated, responding appropriately to a dynamic intracellular Ca2+ landscape.
Journal ArticleDOI
Query-level loss functions for information retrieval
TL;DR: A query-level loss function based on the cosine similarity between a ranking list and the corresponding ground truth is proposed and a coordinate descent algorithm is designed, referred to as RankCosine, which utilizes the proposed loss function to create a generalized additive ranking model.