Comparison of Three Recent Personalization Algorithms

Personalization algorithms recommend products to users based on their previous interactions with the system. The products could be books, movies, or products in a retail system. The earliest personalization algorithms were based on factorization of the user-item matrix where each entry in the matrix would correspond to an interaction, or absence of an interaction of the user with the product. In this article, we compare three recently developed personalization algorithms. The three algorithms are Bayesian Personalized Ranking, Taxonomy Discovery for Personalized Recommendations and Multi-Matrix Factorization. We compare the three algorithms on the hit rate @ position 10 on a held out test set on 1 million users and 200 thousand items in the catalog of Target Corporation. We report our findings in table 1. We develop all three algorithms on an Apache Spark parallel implementation.

Drug-Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking

BioMed Research International ◽

10.1155/2021/6690154 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yihua Ye ◽

Yuqi Wen ◽

Zhongnan Zhang ◽

Song He ◽

Xiaochen Bo

Keyword(s):

Partial Order ◽

Matrix Factorization ◽

Drug Target ◽

Drug Repositioning ◽

Expression Profiles ◽

Learning To Rank ◽

Prediction Performance ◽

Target Interaction ◽

The prediction of drug-target interaction (DTI) is a key step in drug repositioning. In recent years, many studies have tried to use matrix factorization to predict DTI, but they only use known DTIs and ignore the features of drug and target expression profiles, resulting in limited prediction performance. In this study, we propose a new DTI prediction model named AdvB-DTI. Within this model, the features of drug and target expression profiles are associated with Adversarial Bayesian Personalized Ranking through matrix factorization. Firstly, according to the known drug-target relationships, a set of ternary partial order relationships is generated. Next, these partial order relationships are used to train the latent factor matrix of drugs and targets using the Adversarial Bayesian Personalized Ranking method, and the matrix factorization is improved by the features of drug and target expression profiles. Finally, the scores of drug-target pairs are achieved by the inner product of latent factors, and the DTI prediction is performed based on the score ranking. The proposed model effectively takes advantage of the idea of learning to rank to overcome the problem of data sparsity, and perturbation factors are introduced to make the model more robust. Experimental results show that our model could achieve a better DTI prediction performance.

A Survey of Latent Factor Models for Recommender Systems and Personalization

10.36227/techrxiv.13513974.v1 ◽

2021 ◽

Author(s):

Shalin Shah

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Matrix Completion ◽

Factor Models ◽

Singular Value ◽

Latent Factor Models ◽

Value Decomposition ◽

Personalized Ranking ◽

Non Negative Matrix Factorization

Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.

Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12 ◽

Multi-relational matrix factorization using bayesian personalized ranking for social network data

10.1145/2124295.2124317 ◽

2012 ◽

Cited By ~ 75

Author(s):

Artus Krohn-Grimberghe ◽

Lucas Drumond ◽

Christoph Freudenthaler ◽

Lars Schmidt-Thieme

Keyword(s):

Social Network ◽

Matrix Factorization ◽

Network Data ◽

Social Network Data ◽

DMFMDA: Prediction of microbe-disease associations based on deep matrix factorization using Bayesian Personalized Ranking

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2020.3018138 ◽

2020 ◽

pp. 1-1

Author(s):

Yue Liu ◽

ShuLin Wang ◽

Junfeng Zhang ◽

Wei Zhang ◽

Su Zhou ◽

...

Keyword(s):

Matrix Factorization ◽

Disease Associations ◽

A Survey of Latent Factor Models for Recommender Systems and Personalization

10.36227/techrxiv.13513974 ◽

2021 ◽

Author(s):

Shalin Shah

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Matrix Completion ◽

Factor Models ◽

Singular Value ◽

Latent Factor Models ◽

Value Decomposition ◽

Personalized Ranking ◽

Non Negative Matrix Factorization

Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.

Weighted matrix factorization with Bayesian personalized ranking

2017 Computing Conference ◽

10.1109/sai.2017.8252119 ◽

2017 ◽

Cited By ~ 2

Author(s):

Haiyang Zhang ◽

Ivan Ganchev ◽

Nikola S. Nikolov ◽

Zhanlin Ji ◽

Mairtin O'Droma

Keyword(s):

Matrix Factorization ◽

Weighted Matrix ◽

BPR Based Drug Groups and Drug Interaction Extraction

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e7033.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 5379-5384

Keyword(s):

Matrix Factorization ◽

Drug Target ◽

Drug Repurposing ◽

Factorization Method ◽

New Drugs ◽

Ranking Problem ◽

Target Interaction ◽

Interaction Extraction ◽

The key step of drug discovery is the identification of interaction between drug and target proteins. This isn't just valuable to understand the disease, but also assist to distinguishing antagonistic symptoms of drugs. So, in drug repurposing [3] field the drug-target interaction (DTI) prediction is an essential tool. There are various methods to decipher unknown drug-target interaction [2], this is helped in the area of identifying the lead compound in the drug for a specific disease. In this paper proposes drug-target interaction extraction using Bayesian Personalized Ranking (BPR) method [5]. Here it is also solving the ranking problem by the implementation of matrix factorization method [5]. The proposed procedure can manage the occasion of new drugs and takes compound and hereditary resemblances of meds and targets and target tendency into account. [4].

Parallel algorithm for the identification of distributed scatterers in the problem of calculating the velocities of displacements of the earth’s surface by the Persistent Scaterrers method

Вычислительные технологии ◽

10.25743/ict.2021.26.4.008 ◽

2021 ◽

pp. 82-97

Author(s):

Семен Евгеньевич Попов ◽

Вадим Петрович Потапов ◽

Роман Юрьевич Замараев

Keyword(s):

Parallel Implementation ◽

General Scheme ◽

Search Time ◽

Identification Problem ◽

Radar Data ◽

Apache Spark ◽

Massively Parallel ◽

Maximization Problem ◽

Homogeneous Sample ◽

Entire Area

Описывается программная реализация быстрого алгоритма поиска распределенных рассеивателей для задачи построения скоростей смещений земной поверхности на базе платформы Apache Spark. Рассматривается полная схема расчета скоростей смещений методом постоянных рассеивателей. Предложенный алгоритм интегрируется в схему после этапа совмещения с субпиксельной точностью стека изображений временн´ой серии радарных снимков космического аппарата Sentinel-1. Алгоритм не является итерационным и может быть реализован в парадигме параллельных вычислений. Применяемая платформа Apache Spark позволила распределенно обрабатывать массивы стека радарных данных (от 60 изображений) в памяти на большом количестве физических узлов в сетевой среде. Время поиска распределенных рассеивателей удалось снизить в среднем до десяти раз по сравнению с однопроцессорной реализацией алгоритма. Приведены сравнительные результаты тестирования вычислительной системы на демонстрационном кластере. Алгоритм реализован на языке программирования Python c подробным описанием методов и объектов The article describes implementation of the software for a fast algorithm which finds distributed scatterers for the problem of plotting displacement velocities of the earth’s surface based on the Apache Spark platform. The Persistent Scatterer (PS) method is widely used for estimating the displacement rates of the earth’s surface. It consists of the identification of coherent radar targets (interferogram pixels) that demonstrate high phase stability during the entire observation period. The most advanced algorithm for solving the identification problem is the SqueeSAR algorithm. It allows searching and processing Distributed Scatterers (DS) - specific reflectors, integrating them into the general scheme for calculating displacement velocities using the PS method. A careful analysis of the SqueeSAR algorithm has identified areas that are critical to its performance. The whole algorithm is based on an enumeration of the initial data, where nontrivial transformations are performed at each step. The stages of searching for adjacent points in the design window with multiple passes over the entire area of the image and solving the maximization problem when assessing the real values of the interferometric phases turned out to be noticeably costly. To speed up the processing of images, it is proposed to use the Apache Spark massively parallel computing platform. Specialized primitives (Resilient Distributed Data) for recurrent inmemory processing are available here. This provides multiple accesses to the radar data loaded into memory from each cluster node and allows logical dividing of the snapshot stack into subareas. Thus calculations are performed independently in massively parallel mode. Based on the SqueeSAR mathematical model, it is assumed that the radar image data and the calculated geophysical parameters calculated are common for each statistically homogeneous sample of nearby pixels. In accordance with this assumption, the uniformity (homogeneity) of the pixels is estimated within a given window. The search for distributed scatterers occurs independently by the sequence of shifts of the windows over the entire area of the image. The window is shifted along the width and height of the image with a step equal to the width and height of the window. Pairs of samples in the window are composed of vectors of complex pixel values in each of the N images. The validity of the Kolmogorov-Smirnov criterion is checked for each of the pairs. To estimate the values of the phases of homogeneous pixels, the maximization problem is solved. The method of maximum likelihood estimation (MLE) is considered. The construction of the correct MLE form is carried out by analyzing the statistical properties of the coherence matrix of all images using the complex Wishart distribution. The Apache Spark platform applied here permits processing of distributed radar data stack arrays in memory on a large number of physical nodes in a network environment. The average search time for distributed scatterers turned out to be 10 times less compared to the uniprocessor implementation of the algorithm. The algorithm is implemented in the Python programming language with a detailed description of the objects and methods of the algorithm. The proposed algorithm and its parallel implementation allows applying the developed approaches to other problems and types of satellite data for remote sensing of the earth from space

Arithmetic of matrices over rings

10.15407/akademperiodika.430.278 ◽

2021 ◽

Author(s):

V.P. Shchedryk ◽

Keyword(s):

Graduate Students ◽

Normal Form ◽

Matrix Factorization ◽

Ring Theory ◽

Stable Range ◽

Matrix Rings ◽

Principal Ideals ◽

Factorization Theory ◽

Close Relationship ◽

The Matrix

The book is devoted to investigation of arithmetic of the matrix rings over certain classes of commutative finitely generated principal ideals do- mains. We mainly concentrate on constructing of the matrix factorization theory. We reveal a close relationship between the matrix factorization and specific properties of subgroups of the complete linear group and the special normal form of matrices with respect to unilateral equivalence. The properties of matrices over rings of stable range 1.5 are thoroughly studied. The book is intended for experts in the ring theory and linear algebra, senior and post-graduate students.