BROAD-RSI – educational recommender system using social networks interactions and linked data

Pereira, Crystiam Kelle; Campos, Fernanda; Ströele, Victor; David, José; Braga, Regina

doi:10.1186/s13174-018-0076-5

Cited by 36 publications

(33 citation statements)

References 36 publications

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“…In [11], activity frequency of user was drawn from power-law distribution with the objective of improving the recommendation accuracy. Two different aspects, like, social similarity and personalized preference were taken into consideration in [12] recommendation model was designed in [13] based on learning object repositories. A survey of recommender systems for online and mobile social networks (SN) was presented in [14].…”

Section: Related Workmentioning

confidence: 99%

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Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Devi¹,

Pattabiraman²

2020

IJIES

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Recommender systems are in recent past becoming more notable in the age of swift evolution of the information technology ensuring e-commerce users' suitable items. In recent years, various thread-based and trust based approaches have been proposed, which improve the precision and recall of recommendation to certain extent. However, these approaches are less accurate than expected when users' preferences evolve over time and when several aspects of the users are involved. To solve these problems, we propose a novel recommendation method called, Soft Cosine Gradient and Gaussian Joint Probability (SCG-GJP) for Online Social Networks (OSN). Specifically, the Local Soft Cosine Clique Clustering (LSCCC) is first used to identify good candidate items to be recommended. Second, using adjacency matrix factorization, probable past preference of individual user and overall preference of whole community is formed to reduce the problem of mean absolute error. Again, the obtained matrix is trained through the stochastic function gradient to rank the most suitable candidates. Finally, Gaussian Mixture Joint Probability Postfiltering model is proposed to provide useful recommendations. The experiments are executed with real-world dataset (DS) and compared with the conventional recommendation methods. Experimental results demonstrate that the SCG-GJP obtains higher accuracy, and lesser error as well as complexity.

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Section: Related Workmentioning

confidence: 99%

“…With the above past preference of individual user obtained from (11), (12) and overall preference of whole community from (13), (14), recommendations are provided based on the user interactions and generated items via propositions from flipkart.…”

Section: = [ − ∑ =1mentioning

confidence: 99%

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Devi¹,

Pattabiraman²

2020

IJIES

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“…As ontologias são utilizadas para modelar os estudantes e para adequar os componentes do processo de aprendizagem aos alunos, tais como OAs, atividades de aprendizagem preferidas e métodos de ensino-aprendizagem relevantes. [Pereira et al 2018] criaram uma infraestrutura para a recomendação de recursos educacionais com base em informações como o perfil do usuário e o contexto educacional, extraídas da rede social Facebook. Tecnologias da WS e técnicas de extração de informações são utilizadas para extrair, enriquecer e definir os perfis e interesses dos usuários.…”

Section: Trabalhos Relacionadosunclassified

Avanços na recomendação personalizada de objetos de aprendizagem através da utilização de meta-heurísticas clássicas associadas aos Problemas de Cobertura de Conjuntos e de Máxima Cobertura: Uma análise experimental

Júnior¹,

Dorça²,

Andrade³

et al. 2020

Anais Do XXXI Simpósio Brasileiro De Informática Na Educação (SBIE 2020)

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Uma limitação dos sistemas de recomendação é não garantir que os objetos de aprendizagem (OAs) recomendados cobrem todos os conceitos que o estudante precisa aprender. Para lidar com esse desafio, neste artigo, o Problema de Recomendação de Objetos de Aprendizagem (PROA) é formulado como o Problema de Cobertura de Conjuntos e um problema similar ao Problema de Máxima Cobertura, ambos da classe NP-Hard. Adaptam-se meta-heurísticas gulosas e evolucionárias mostrando que elas são apropriadas para a solução do PROA. Este artigo contribui com o estado da arte com uma recomendação de OAs mais personalizada usando uma ontologia que modela as necessidades de conhecimento do estudante e o seu estilo de aprendizagem.

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“…The first concern is the identification of data sources. Data may be collected within the system (e.g., from purchase transactions), derived from external sources (e.g., open data (Di Noia et al, 2012)), linked data (Pereira et al, 2018)), or created through combining knowledge from different data sets (Li & Zaïane, 2004). Sourced data can either be explicit, i.e., preference data created by the user (e.g., item ratings), or implicit, i.e., preference data inferred from the behavior of users (e.g., monitoring user's item page visits) (Bobadilla et al, 2013).…”

Section: Domain Characteristicsmentioning

confidence: 99%

The recommender canvas: A model for developing and documenting recommender system design

Capelleveen

Amrit

Yazan

et al. 2019

Expert Systems with Applications

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The task of designing a recommender system is a complex process. Because of the many technological advancements that may be included in a recommender system, engineers are faced with a fast growing number of design related decisions to be taken. Unfortunately, there is no general approach yet for decision makers that can act as a framework guiding the design of a recommender system. The rich collection of literature on recommender systems, though, offers a great source to identify the key areas where these decisions need to be taken. In this paper, we survey existing literature with the aim of building a recommender system model inspired by Osterwalder's canvas theory. The result of our semi-structured synthesis is a novel design approach in the form of a canvas for designing recommender systems. This work provides a better understanding and can serve as a guide for decision making in recommender system design.

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BROAD-RSI – educational recommender system using social networks interactions and linked data

Cited by 36 publications

References 36 publications

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Avanços na recomendação personalizada de objetos de aprendizagem através da utilização de meta-heurísticas clássicas associadas aos Problemas de Cobertura de Conjuntos e de Máxima Cobertura: Uma análise experimental

The recommender canvas: A model for developing and documenting recommender system design

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