Recommendation System for Wedding Service Organizer using Content-Boosted Collaborative Filtering Methods

Kesuma, Rahman Indra; Iqbal, Amirul

doi:10.1088/1755-1315/537/1/012024

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…An inclusion hybrid is to integrate one recommendation algorithm into the framework of another recommendation algorithm. For example, in order to solve the sparsity problem of collaborative filtering recommendation algorithms, feature extraction is performed by content-based recommendation algorithms, which in turn enrich the preference model of users, as a way to calculate the similarity between users [17]. The last complementary mixture is obtained by selecting one recommendation algorithm to obtain the initial points or parameters, etc., necessary for another recommendation algorithm, such as the parameters of a Bayesian mixed-effects regression model through a Markov chain Monte Carlo approach [18].…”

Section: Recommender Systemmentioning

confidence: 99%

SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information

Patterson¹,

Wilson²,

Forrow³

et al. 2023

Preprint

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With the rapid development of the Internet industry, the problem of information overload has arisen due to the abundance of information available online. Recommendation algorithms, as the core of recommendation systems, have been attracting much attention and are a hot topic of research for many experts and scholars. The classical recommendation algorithms are mainly divided into three major categories: collaborative filtering recommendation algorithms, content-based recommendation algorithms, and hybrid-based recommendation algorithms. Although these algorithms are widely used in various fields, with the proliferation of information, these traditional recommendation algorithms are no longer able to meet the needs of the times. To address this issue, recommendation systems have been developed to provide users with personalized and relevant information or products. Despite the wide use of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches, traditional recommendation algorithms have limitations and are no longer suitable for meeting the demands of the times. This paper proposes a new recommendation algorithm, SFRRG, that fuses structure and feature information in graph neural networks to improve the performance of the recommendation system in rating prediction. The effectiveness of the proposed algorithm is demonstrated through experiments on various data sets and compared with existing recommendation algorithms.

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Section: Recommender Systemmentioning

confidence: 99%