Deep learning in citation recommendation models survey

Ali, Zafar; Kefalas, Pavlos; Muhammad, Khan; Ali, Bahadar; Imran, Muhammad

doi:10.1016/j.eswa.2020.113790

Cited by 65 publications

(24 citation statements)

References 49 publications

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“…We will denote this encoding as u (emb) , and the resulting clusters obtained in this embedding will be denoted as Ω(U (emb) ) . The embedding can be obtained using deep learning [2,7,9,26,41] or matrix factorization techniques [27,32,33]. In this paper, since we use matrix factorization to obtain the utility function (as we will see in the next section), we have decided to use this same technique to calculate the embedding that projects users in this new vectorial space.…”

Section: User Encodingmentioning

confidence: 99%

User encoding for clustering in very sparse recommender systems tasks

Pérez-Núñez

Díez

Luaces

et al. 2021

Multimed Tools Appl

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Recommender Systems are a very useful tool which let companies and service providers focus in the preferences of their customers, helping them to avoid an overwhelming variety of choices. In this context, clustering tools can play an important role to detect groups of customers with similar tastes. Thus, companies can make personalized marketing campaigns, offering to their users new products which have been consumed by other users with comparable preferences. In this paper we present a general framework to cluster users with respect to their tastes when the registers stored about the interactions between users and products are extremely scarce. Commonly, clustering methods employ the values of features describing the samples to be clustered (users in our case), but such features are not always available. We propose some alternative representations for users, in which their tastes are gathered to some extent, so that clustering algorithms can take advantage and make more homogeneous groups in this regard. To illustrate the performance of the whole framework, we tested it on six popular datasets commonly used as a benchmark for recommender systems, as well as on an extremely sparse real-world dataset that records the preferences of readers to click promoted links in digital publications. In the experimental section we compare our proposed representations to other common user encodings. We show that clustering users attending only to their feature values or to the items they have evaluated gives rise to the worst scores in terms of taste homogeneity.

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Section: User Encodingmentioning

confidence: 99%

User encoding for clustering in very sparse recommender systems tasks

Pérez-Núñez

Díez

Luaces

et al. 2021

Multimed Tools Appl

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“…Another study proposes a novel embedding-based neural network model for citation recommendation that captures the relatedness and importance of words in the context [22]. The study of Ali et al reviews the application of deep learning in the domain of citation recommendation [23]. With the advance in text processing technologies in machine learning, content-based approaches are expected to yield better performance.…”

Section: Content-based Approachesmentioning

confidence: 99%

A Hybrid Approach for Paper Recommendation

Kang

Hou

Zhao

et al. 2021

IEICE Trans. Inf. & Syst.

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Paper recommendation has become an increasingly important yet challenging task due to the rapidly expanding volume and scope of publications in the broad research community. Due to the lack of user profiles in public digital libraries, most existing methods for paper recommendation are through paper similarity measurements based on citations or contents, and still suffer from various performance issues. In this paper, we construct a graphical form of citation relations to identify relevant papers and design a hybrid recommendation model that combines both citationand content-based approaches to measure paper similarities. Considering that citations at different locations in one article are likely of different significance, we define a concept of citation similarity with varying weights according to the sections of citations. We evaluate the performance of our recommendation method using Spearman correlation on real publication data from public digital libraries such as CiteSeer and Wanfang. Extensive experimental results show that the proposed hybrid method exhibits better performance than state-of-the-art techniques, and achieves 40% higher recommendation accuracy in average in comparison with citation-based approaches.

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“…A personalized system for recommending auxiliary materials was proposed depending on the degree of difficulty of the auxiliary documents, the individual learning styles and the specific course topics (Ali et al, 2020). The proposal is based on several studies in which the effects of using Facebook were studied in various aspects of education and a learning platform was used for the exchange of auxiliary materials.…”

Section: The Use Of Educational Recommendation Systemsmentioning

confidence: 99%

Design and implementation of the multi-agent system in education

Hamal

Faddouli

Harouni

2021

WJET

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Nowadays, AI is a real springboard for finding solutions to optimize and improve learning and teaching processes. This issue has been a focus of humanity for millennia, and very significant advances have been made in this quest. This article aims to address the issue of optimizing and improving learning and teaching processes through AI (Artificial Intelligence), considering crossroads of research fields AIED (Artificial Intelligence in Education), EDM (Educational Data Mining) and LA (Learning Analytic). The research made use of secondary data collected from previous research on the topic and primary data was collected using a case study. A comparative analysis was conducted and based on this opportunity, we propose a multi-agent system based on AI techniques, which is capable of performing broader analyses of learning and teaching processes. The research also implemented a prototype of EMAS. Through this system, teachers and learners will be able to access a wide range of relevant and reliable information about learning and teaching processes. Key words: AIED, EDM, EMAS, LA, recommendation system, education dropping out, emotion detection

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Deep learning in citation recommendation models survey

Cited by 65 publications

References 49 publications

User encoding for clustering in very sparse recommender systems tasks

User encoding for clustering in very sparse recommender systems tasks

A Hybrid Approach for Paper Recommendation

Design and implementation of the multi-agent system in education

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