Analisis dan Implementasi Kernelized Bayesian Matrix Factorization pada Filtrasi Kolaboratif dalam Studi Kasus Sistem Rekomendasi

Recommender Systems merupakan sistem yang memberikan informasi rekomendasi kepada pengguna terhadap suatu data. Pengambilan dan penyaringan informasi tersebut terkadang cukup sulit dilakukan karena jumlah data yang besar dan persebaran data yang rendah sehingga diperlukan metode yang cukup baik serta tepat untuk menangani masalah tersebut. Salah satu cara untuk menanganinya adalah dengan menggunakan metode yang dapat mengatasi masalah tersebut, salah satunya adalah menggunakan Collaborative Filtering, atau lebih spesifik dengan menggunakan Matrix Factorization. Namun, hal tersebut tidak cukup dikarenakan Matrix Factorization tidak dapat mengatasi persebaran data yang rendah dengan baik. Untuk itu, penulis mengusulkan untuk mengembangkan teknik tersebut dengan menggunakan metode kernel dan bayesian probabilistic yang disebut Kernelized Bayesian Matrix Factorization. Metode tersebut mengefesiensikan proses prediksi dan memaksimalkan tingkat akurasi dibandingkan dengan menggunakan Matrix Factorization. Dalam pengujiannya, penulis menggunakan datasets movielens dengan satu juta ratings dan mendapatkan performa metode Kernelized Bayesian Matrix Factorization tidak begitu baik dalam hal waktu untuk melakukan proses latih, namun cukup baik dalam proses uji dikarekan adanya peningkatan akurasi dan penurunan nilai error. Kata Kunci : collaborative filtering, recommender system, matrix factorization, kernelized bayesian matrix factorizatio

GPstruct: Bayesian structured prediction using Gaussian processes

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M ^3 N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct

Large Scale Tensor Regression using Kernels and Variational Inference

We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method \textit{Kernel Fried Tensor}(KFT) and present it as a large scale forecasting tool for high dimensional data. Our results show superior performance against \textit{LightGBM} and \textit{Field Aware Factorization Machines}(FFM), two algorithms with proven track records widely used in industrial forecasting. We also develop a variational inference framework for KFT and associate our forecasts with calibrated uncertainty estimates on three large scale datasets. Furthermore, KFT is empirically shown to be robust against uninformative side information in terms of constants and Gaussian noise

Learning Output Kernels for Multi-Task Problems

Simultaneously solving multiple related learning tasks is beneficial under a variety of circumstances, but the prior knowledge necessary to correctly model task relationships is rarely available in practice. In this paper, we develop a novel kernel-based multi-task learning technique that automatically reveals structural inter-task relationships. Building over the framework of output kernel learning (OKL), we introduce a method that jointly learns multiple functions and a low-rank multi-task kernel by solving a non-convex regularization problem. Optimization is carried out via a block coordinate descent strategy, where each subproblem is solved using suitable conjugate gradient (CG) type iterative methods for linear operator equations. The effectiveness of the proposed approach is demonstrated on pharmacological and collaborative filtering data

VB-MK-LMF: Fusion of drugs, targets and interactions using Variational Bayesian Multiple Kernel Logistic Matrix Factorization

Background Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance. Method We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions. Results VB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of ``small sample size'' regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purpose graphics processing units yielding significantly improved computational time. Conclusion In standard benchmarks, VB-MK-LMF shows significantly improved predictive performance in a wide range of settings. Beyond these benchmarks, another contribution of our work is highlighting and providing estimates for further pharmaceutically relevant quantities, such as promiscuity, druggability and total number of interactions. Availability Data and code are available at http://bioinformatics.mit.bme.hu