Recommendation system based on semantic scholar mining and topic modeling on conference publications

Jelodar, Hamed; Wang, Yongli; Xiao, Gang; Rabbani, Mahdi; Zhao, Ruxin; Ayobi, Seyedvalyallah; Hu, Peng; Masood, Isma

doi:10.1007/s00500-020-05397-3

Cited by 15 publications

(15 citation statements)

References 57 publications

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“…Extracting the domain of papers [10,13,24,25,[28][29][30][31] Extracting the domain of articles and creating profile of articles for recommendation Big data platform [30,31] They are implemented using big data system platforms, which are suitable for systems with huge data c. How has the relevance of textual information in recommender systems for the academic domain previously used by researchers been discovered and understood.…”

Section: Used By Descriptionmentioning

confidence: 99%

“…The article [25] was done in three stages, in the first stage: pre-processing of the text of the articles was done (removing noise and stopwords). The second stage was the semantic operation, which was used to discover knowledge and extract meanings from Gibbs sampling.…”

Section: Extracting the Domain Of Papersmentioning

confidence: 99%

“…In step 3, after semantic extraction and topic extraction, in this step keywords are generated as an explosive topic based on ranking and high probability. These topics are recommended as data science research areas (Figure 1) [25].…”

Section: Extracting the Domain Of Papersmentioning

confidence: 99%

“…In reference [30] paper presented a method based on the background information of the papers in the Hadoop platform. They collected a dataset of conference papers from 2013 to 2015 of the IEEE Digital Library in the three domains of computer, electrical, and mechanical using a crawler that received the URL of each paper as The research framework for recommending research field and semantic mining from scholar's paper [25].…”

Section: Hadoop Platformmentioning

confidence: 99%

See 3 more Smart Citations

Paper Recommender System Using Big Data Tools

Jokar¹,

Esfandiari²,

Aghamirzadeh³

et al. 2024

Optimization Algorithms - Classics and Recent Advances

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To face the problem of information overload, digital libraries, like other businesses, have used recommender systems and try to personalize recommendations to users by using the textual information of papers. This textual information includes title, abstract, keywords, publisher, author and other similar items. Since the volume of papers is increasing day by day and recommender systems do not have the ability to cover this huge volume to process papers according to the user’s tastes, that is why we need to use our papers to cover and process this volume quickly. We have big data tools, which will offer relevant recommendations by running parallel processing. In this chapter, the researches and researches of researchers in the field of recommender systems/aware of the text of scientific papers and recommender systems have been discussed.

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Section: Used By Descriptionmentioning

confidence: 99%

Section: Extracting the Domain Of Papersmentioning

confidence: 99%

Section: Extracting the Domain Of Papersmentioning

confidence: 99%

Section: Hadoop Platformmentioning

confidence: 99%

See 2 more Smart Citations

Paper Recommender System Using Big Data Tools

Jokar¹,

Esfandiari²,

Aghamirzadeh³

et al. 2024

Optimization Algorithms - Classics and Recent Advances

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show abstract

“…collaborative filtering-based (CF) [2], knowledge-based (KB) [26], hybrid recommender systems [17,27,28]. Among them, hybrid recommenders have gained much attention in recent years.…”

Section: Recommender Systemsmentioning

confidence: 99%

Hybrid recommender system with core users selection

Jin

et al. 2022

Soft Comput

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Recommender system plays an increasingly important role in identifying the individual's preference and accordingly makes a personalized recommendation. Matrix factorization is currently the most popular model-based collaborative filtering (CF) method that achieves high recommendation accuracy. However, similarity computation hinders the development of CF-based recommendation systems. Preference obtained only depends on the explicit rating without considering the implicit content feature, which is the root cause of preference bias. In this paper, the content feature of items described by fuzzy sets is integrated into the similarity computation, which helps to improve the accuracy of user preference modelling. The importance of a user is then defined according to preferences, which serves as a baseline standards of the core users selection. Furthermore, core users based matrix factorization model (CU-FHR) is established, then genetic algorithm is used to predict the missing rating on items. Finally, MovieLens is used to test the performance of our proposed method.Experiments show CU-FHR achieves better accuracy in prediction compared with the other recommendation methods.

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