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The effectiveness of existing top-<i>N</i> recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-<i>N</i> recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned(More)
Structure-activity relationship (SAR) models are used to inform and to guide the iterative optimization of chemical leads, and they play a fundamental role in modern drug discovery. In this paper, we present a new class of methods for building SAR models, referred to as multi-assay based, that utilize activity information from different targets. These(More)
Mining chemical compounds in silico has drawn increasing attention from both academia and pharmaceutical industry due to its effectiveness in aiding the drug discovery process. Since graphs are the natural representation for chemical compounds , most of the mining algorithms focus on mining chemical graphs. Chemical graph mining approaches have many(More)
Fully-observable high-order Boltzmann Machines are capable of identifying explicit high-order feature interactions theoretically. However , they have never been used in practice due to their prohibitively high computational cost for inference and learning. In this paper, we propose an efficient approach for learning a fully-observable high-order Boltz-mann(More)
Bio-relation extraction (bRE), an important goal in bio-text mining, involves subtasks identifying relationships between bio-entities in text at multiple levels, e.g., at the article, sentence or relation level. A key limitation of current bRE systems is that they are restricted by the availability of annotated corpora. In this work we introduce a(More)
We propose a Multi-task Multi-dimensional Hawkes Process (MMHP) for modeling event sequences where there exist multiple triggering patterns within sequences and structures across sequences. MMHP is able to model the dynamics of multiple sequences jointly by imposing structural constraints and thus systematically uncover clustering structure among sequences.(More)