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Recently, some recommendation methods try to improve the prediction results by integrating information from user's multiple types of behaviors. How to model the dependence and independence between different behaviors is critical for them. In this paper, we propose a novel recommendation model, the Group-Sparse Matrix Factorization (GSMF), which factorizes(More)
Traditionally, Collaborative Filtering assumes that similar users have similar responses to similar items. However, human activities exhibit heterogenous features across multiple domains such that users own similar tastes in one domain may behave quite differently in other domains. Moreover, highly sparse data presents crucial challenge in preference(More)
n-3 Polyunsaturated fatty acids (n-3 PUFA) are important for human health. Alternative resources of n-3 PUAFs created by transgenic domestic animals would be an economic approach. In this study, we generated a mfat-1 transgenic cattle expressed a Caenorhabditis elegans gene, mfat-1, encoding an n-3 fatty acid desaturase. Fatty acids analysis of tissue and(More)
Collaborative filtering with implicit feedbacks has been steadily receiving more attention, since the abundant implicit feedbacks are more easily collected while explicit feedbacks are not necessarily always available. Several recent work address this problem well utilizing pairwise ranking method with a fundamental assumption that a user prefers items with(More)
The rice Xa13 gene, whose promoter harbors a UPT (up-regulated by transcription activator-like [TAL] effector) box, UPT(PthXo1), plays a pivotal role in the race-specific pathogenicity caused by Xanthomonas oryzae pv. oryzae (Xoo) strain PXO99. PXO99 causes rice disease by inducing Xa13. It is unknown, however, whether the UPT(PthXo1) box is the only(More)