An anatomization of research paper recommender system: Overview, approaches and challenges

Sharma, Ritu; Gopalani, Dinesh; Meena, Yogesh Kumar

doi:10.1016/j.engappai.2022.105641

Cited by 17 publications

(4 citation statements)

References 143 publications

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“…For an example of sentiment analysis using the R package sentimentr on Twitter posts, see Figure 7. The posts are identified by a number (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). They are ranked as either positive (green), neutral (gray), or negative (red), and each has a number denoting the strength of the sentiment.…”

Section: Resultsmentioning

confidence: 99%

“…The optimum number of topics k is determined using a harmonic mean method, as determined by Griffiths and Steyvers [76,77]. This is shown in Equation (5).…”

Section: Generating the Topic Modelsmentioning

confidence: 99%

“…Fake user profiles can easily manipulate recommendation results by giving the highest rates to targeted items and rate other items similar to regular profiles. This behavior is called a "shilling attack" [5].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

McGarry

2023

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In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts, and sentiment.

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Section: Resultsmentioning

confidence: 99%

“…The optimum number of topics k is determined using a harmonic mean method, as determined by Griffiths and Steyvers [76,77]. This is shown in Equation (5).…”

Section: Generating the Topic Modelsmentioning

confidence: 99%

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Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

McGarry

2023

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“…Digital libraries are overloaded, making it time-consuming to find relevant paper. Literature search takes time, as such we need intelligent systems to make it to suggest for pertinent articles among millions available (Sharma et al, 2023).…”

Section: Overview On Recommender Systemmentioning

confidence: 99%

Hybrid-based Research Article Recommender System

Teh,

Haw,

Santoso

2023

ijmst

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A recommender system, which might assist in providing clients with new information and a better experience, is becoming increasingly popular in this era of modernization. Recommender systems are often used by various platforms to provide new products to consumers, which may also help in improving product sales. Additionally, the recommender system is essential in academic domains. It is common for users to take a while to find and access the materials they need. The recommender system is now available, which could reduce the time spent looking for materials and improve student achievement. Therefore, it is crucial to explore more on the theory and implementation of the recommender system. This paper aims to study a few types of recommender system techniques and implement it in the research article recommender system. Additionally, related research on each of the three recommender systems will be reviewed, along with a description of the related study, the dataset used, and the evaluation method.

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Edge‐Optimized Neural Networks for Real‐Time English Corpus Recommendation

Han,

Zhang

2024

Internet Technology Letters

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This paper presents a lightweight framework for real‐time English corpus recommendation optimized for edge computing environments. Traditional recommendation systems, though effective in modeling complex user‐content interactions, often suffer from excessive computational demands, making them unsuitable for deployment on resource‐constrained edge devices. To tackle these limitations, we introduce a novel solution that efficiently balances advanced interaction modeling with computational cost optimization. The framework integrates a robust baseline model that captures rich user‐content interactions while employing knowledge distillation techniques to transfer critical information, ensuring that high‐quality recommendations are maintained even with reduced computational overhead. Our approach leverages real‐time adaptability to edge environments, providing both speed and accuracy. Experimental evaluations on benchmark datasets reveal significant reductions in response time, without compromising the accuracy of recommendations, thus making it ideal for real‐time applications in edge computing scenarios.

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An anatomization of research paper recommender system: Overview, approaches and challenges

Cited by 17 publications

References 143 publications

Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

Hybrid-based Research Article Recommender System

Edge‐Optimized Neural Networks for Real‐Time English Corpus Recommendation

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