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This paper focuses on developing effective and efficient algorithms for top-N recommender systems. A novel Sparse Linear Method (SLIM) is proposed, which generates top-N recommendations by aggregating from user purchase/rating profiles. A sparse aggregation coefficient matrix W is learned from SLIM by solving an `1-norm and `2-norm regularized optimization(More)
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)
The increasing amount of side information associated with the items in E-commerce applications has provided a very rich source of information that, once properly exploited and incorporated, can significantly improve the performance of the conventional recommender systems. This paper focuses on developing effective algorithms that utilize item side(More)
This paper focuses on developing classification algorithms for problems in which there is a need to predict the class based on multiple observations (examples) of the same phenomenon (class). These problems give rise to a new classification problem, referred to as set classification, that requires the prediction of a set of instances given the prior(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)
4 GNWI-Gesellschaft fuer naturwissenschaftliche Informatik mbH, Oer-Erkenschwick, Germany The remaining simulation box bulk volume is usually filled up in a random manner Handbook of chemoinformatics from data to knowledge. (pages 548-576) As a Professor Emeritus she has continued her research on the knowledge. analysis based on the logP data and Hammett(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)
The silkworm, Bombyx mori, is an important economic insect for silk production. However, many of the mature peptides relevant to its various life stages remain unknown. Using RP-HPLC, MALDI-TOF MS, and previously identified peptides from B. mori and other insects in the transcriptome database, we created peptide profiles showing a total of 6 ion masses that(More)