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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)
In this study, interactions between Cry1Ac, a toxic crystal protein produced by Bacillus thuringiensis (Berliner), and Beauveria bassiana on the mortality and survival of Ostrinia furnacalis was evaluated in the laboratory. The results showed that Cry1Ac is toxic to O. furnacalis. Not only were larval growth and development delayed, but pupation, pupal(More)
Diabetic retinopathy is one of the common complications of diabetes and is the leading cause for patients' visual dysfunction and sight loss. However, the mechanism of diabetic retinopathy is not clearly defined. The present study was undertaken to investigate neuroretinal apoptosis in different stages in a mouse model for type 2 diabetes mellitus and the(More)
Multi-view learning arouses vast amount of interest in the past decades with numerous real-world applications in web page analysis, bioinformatics, image processing and so on. Unlike the most previous works following the idea of co-training, in this paper we propose a new generative model for Multi-view Learning via Probabilistic Latent Semantic Analysis,(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)
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)
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)
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)