Recommending items to group of users using Matrix Factorization based Collaborative Filtering

Ortega, Fernando; Hernando, A.; Bobadilla, Jesús; Kang, Jeon Hyung

doi:10.1016/j.ins.2016.01.083

Cited by 147 publications

(72 citation statements)

References 30 publications

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“…Currently, CF approaches are mainly classified into k‐nearest neighbor‐based CF, model‐based CF and hybrid CF, in which MF is the most popular model . MF techniques such as regularized matrix factorization and non‐negative matrix factorization have been successfully applied to CF .…”

Section: Related Workmentioning

confidence: 99%

Collaborative filtering and association rule mining‐based market basket recommendation on spark

Wang

Wen

Guo

et al. 2019

Concurrency and Computation

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Summary Traditional market basket recommendation approaches normally cannot well recommend unpopular commodities in big data environment. To address such problem and deal with large datasets of practical supermarkets, this paper presents a market basket recommendation framework and proposes an Extended algorithm based on Collaborative Filtering and Association Rule mining, named ECFAR. The ECFAR covers two sub‐algorithms. First, a parallel FP‐Growth algorithm is used for mining association rules on Spark, which is designed to increase the efficiency of processing big data. Then, a parallel similar commodity discovery method based on matrix factorization is proposed. By analyzing a real‐world sales dataset collected from a local supermarket group, extensive experiments are conducted to verify its effectiveness.

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Section: Related Workmentioning

confidence: 99%

Collaborative filtering and association rule mining‐based market basket recommendation on spark

Wang

Wen

Guo

et al. 2019

Concurrency and Computation

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show abstract

“…The effect of the recommended service was not good. Ortega et al [10] proposed a matrix decomposit-ion method to build a group recommendation system, which had three decomposition strategies. But the matrix decompos-etion method had the disadvantage that the recommended list is less interpretable.…”

Section: Related Workmentioning

confidence: 99%

“…Many scholars focused on existing and non-existing group recommendations, user interest modeling, group rating prediction accuracy and recommendation accuracy and other issues. They have put forward some recommendation algorithms and designed some experimental prototype system [5,6,7,8,9,10] . But much of the work does not take into consideration in the differences in the level of concern of different groups for each category of project, as well as differences in the level of concern among members of the same group for each category of item.…”

Section: Introductionmentioning

confidence: 99%

A Group Recommendation Algorithm Based on Group Members' Weights and Project Popularity

Min¹,

Wu²,

Li³

2017

Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC

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With the rapid development of many types of social networks, the number of people involved in group activities is increasing, and thus group recommendation systems have been widely studied. This paper proposes a collaborative filtering algorithm to mine groups' interests. In view of the different roles played by different users in the group, the algorithm combines the members' weights factor. And because of the differences in the degree of concern among groups and members for the various categories, the weighted mean similarity method is used. In order to solve the problem of a groups' narrow field of view of the existing algorithms and to more effectively mine unpopular items, the algorithm considers project popularity to improve the novelty of the recommendations. Experiments on the MovieLens 1M GroupLens data set show that the algorithm can effectively improve the accuracy of the recommendations. And to a certain extent, it improves the novelty of the ecommendations.

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“…Esto para dar una recomendación de las materias que debería tomar un estudiante de sexto semestre de Ingeniería Electrónica y Telecomunicaciones ofertada por la FIEC -ESPOL. Ortega et al (2016) proponen recomendación de ítems por filtrado colaborativo aplicando factorización de matrices. Se realizaron pruebas con una base de datos de Netflix y de MovieLens para demostrar que el desempeño de un sistema de recomendación para grupos depende del tamaño de este.…”

Section: Trabajos Relacionadosunclassified

Sistema de recomendación de objetos de aprendizaje a través de filtrado colaborativo

et al. 2016

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El uso de plataformas colaborativas en educación se ha incrementado en los últimos años. La gran cantidad de usuarios registrados en las mismas ha originado el problema de no saber con quién interactuar. Muchas veces la persona con quien se interactúa no es la idónea y resultan interacciones no exitosas. En este artículo, se presenta un sistema recomendado que sugiere personas idóneas para interactuar en una plataformacolaborativa.

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Recommending items to group of users using Matrix Factorization based Collaborative Filtering

Cited by 147 publications

References 30 publications

Collaborative filtering and association rule mining‐based market basket recommendation on spark

Collaborative filtering and association rule mining‐based market basket recommendation on spark

A Group Recommendation Algorithm Based on Group Members' Weights and Project Popularity

Sistema de recomendación de objetos de aprendizaje a través de filtrado colaborativo

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