Personalized recommender system based on friendship strength in social network services

Seo, Young-Duk; Kim, Young-Gab; Lee, Euijong; Baik, Doo Kwon

doi:10.1016/j.eswa.2016.10.024

Cited by 91 publications

(41 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Jiang, Cui, Wang, Zhu, and Yang () incorporated individual preference and interpersonal influence into probabilistic matrix factorization, and it was the combination of social information and content‐based method. Seo, Kim, Lee, and Baik () combined friendship strength into collaborative filtering method to recommend topics. In existing studies that combine social relations and content‐based methods, only preference information towards target items is considered.…”

Section: Literature Reviewmentioning

confidence: 99%

A social recommendation system for academic collaboration in undergraduate research

Liu

Yang

et al. 2018

Expert Systems

View full text Add to dashboard Cite

Academic collaboration plays an important role in undergraduate research. Current methods rely on offline social contacts for undergraduate students to collaborate with academic staff members in universities and research institutions. In big data era, it is difficult for undergraduate students to find suitable research project opportunities and supervisors to work with. This paper proposes a social recommendation system for undergraduate students to find research project opportunities and work with research project teams on an academic collaboration network. The proposed recommendation method integrates relevance, connectivity, and quality modules, where profiles of undergraduates are constructed with their self‐claimed information, research activities (e.g., studying and reading research publications and reading research projects), and social connections in the academic collaboration network. Suitable research projects are recommended based on the undergraduates' profiles. Experiments are conducted, and the results have shown that the proposed social recommendation system can facilitate undergraduates' selection of research projects.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A social recommendation system for academic collaboration in undergraduate research

Liu

Yang

et al. 2018

Expert Systems

View full text Add to dashboard Cite

show abstract

“…As another example, existing recommender systems on different websites, take social networks and online shopping stores an instance, for example Amazon, it possesses one of the most powerful recommender systems, having bought a digital camera by customers, they usually order memory cards or tripods as well, this is a pattern that has been gradually discovered by Amazon recommender system, and it provides suggestions to users according to the patterns which are oriented with prerequisites of this pattern. The YouTube or Facebook social network recommender system has almost the same performance and follows the same mechanism [16]. All of these applications which we face with every day are done by analyzing the collected data in the past and they are successful samples of utilizing data mining in daily life.…”

Section: Data Miningmentioning

confidence: 99%

Big data Analysis in Plant Science and Machine Learning Tool Applications in Genomics and Proteomics

Khiarak

Valizadeh-Kamran

Heydariyan

et al. 2018

International Journal of Computational and Experimental Science and Engineering

View full text Add to dashboard Cite

Data extensions in plant biology and drastically increasing data volume in this field impose the scientists analyzing data by means of smart computer systems. Since, manually analyzing huge amount of data is cumbersome and even impossible. A comparative study of proteins a wide scale, is the proteomics knowledge. Nowadays, the proteomics analysis is considered as one of the most important methods in genomics and of the gene expression studies. Large amounts of data are big challenges in plant biology. Biological communities either need to create data making compatible with the parallel computing and the data management associated with its infrastructures or are looking for novel analytical patterns to extract information from a large amount of data. Machine learning provides promising analytical and computational solutions for large, heterogeneous, non-structured datasets for large-scale data, especially for the proteomics data. In particular, a conceptual review and applicable methods of machine learning are described by predicting that how machine learning with massive data technology can be an interface to facilitate basic researches and biotechnology plant sciences.

show abstract

“…Ding, Liu, Duan, and Nie ) proposed a mining model on the basis of convolution neural network for identifying whether the user has a consumption intention, so that better tailored products or services can be recommended. Seo, Kim, Lee, and Baik () proposed a recommendation system on the basis of friendship strength in social network services. The hybrid recommendation systems combine the two aforementioned techniques (Barragáns‐Martinez et al, ).…”

Section: Related Workmentioning

confidence: 99%

Personalized question recommendation for English grammar learning

et al. 2017

View full text Add to dashboard Cite

Learning English grammar is a very challenging task for many students especially for nonnative English speakers. To learn English well, it is important to understand the concepts of the English grammar with lots of practise on exercise questions. Previous recommendation systems for learning English mainly focused on recommending reading materials and vocabulary. Different from reading material and vocabulary recommendations, grammar question recommendation should recommend questions that have similar grammatical structure and usage to the question of interest. The content similarity calculation methods used in existing recommendation methods cannot represent the similarity between grammar questions effectively. In this paper, we propose a content‐based approach for personalized grammar question recommendation, which recommends similar grammatical structure and usage questions for further practising. Specifically, we propose a novel structure named parse‐key tree to capture the grammatical structure and usage of grammar questions. We then propose 3 measures to compute the similarity between the question query and database questions for grammar question recommendation. Additionally, we incorporated the proposed recommendation method into a Web‐based English grammar learning system and presented its performance evaluation in this paper. The experimental results have shown that the proposed approach outperforms other classical and state‐of‐the‐art methods in recommending relevant grammar questions.

show abstract

Personalized recommender system based on friendship strength in social network services

Cited by 91 publications

References 17 publications

A social recommendation system for academic collaboration in undergraduate research

A social recommendation system for academic collaboration in undergraduate research

Big data Analysis in Plant Science and Machine Learning Tool Applications in Genomics and Proteomics

Personalized question recommendation for English grammar learning

Contact Info

Product

Resources

About