Paper recommendation based on heterogeneous network embedding

Ali, Zafar; Qi, Guilin; Muhammad, Khan; Ali, Bahadar; Abro, Waheed Ahmed

doi:10.1016/j.knosys.2020.106438

Cited by 60 publications

(22 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several other studies have used data from scientific articles, such as topical relevance, citation proximity, article tags, etc. for recommendations in a network-based framework (Ali et al, 2020). Bibliographic coupling has also been used to create a co-citation based network, alongside a weighted in-text citation scoring method, to increase suggestions of most commonly cited articles (Habib & Afzal, 2019).…”

Section: Related Workmentioning

confidence: 99%

Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis

Lathabai

Nandy

Singh

2022

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis

Lathabai

Nandy

Singh

2022

Expert Systems with Applications

View full text Add to dashboard Cite

“…Most of these categories are defined precisely but graph-based approaches are not always characterised concisely: Content-based filtering (CBF) methods are said to be ones where user interest is inferred by observing their historic interactions with papers [ 9 , 16 , 58 ]. Recommendations are composed by observing features of papers and users [ 5 ]. In collaborative filtering (CF) systems the preferences of users similar to a current one are observed to identify likely relevant publications [ 9 , 16 , 58 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Scientific paper recommendation systems: a literature review of recent publications

Kreutz

Schenkel

2022

Int J Digit Libr

View full text Add to dashboard Cite

Scientific writing builds upon already published papers. Manual identification of publications to read, cite or consider as related papers relies on a researcher’s ability to identify fitting keywords or initial papers from which a literature search can be started. The rapidly increasing amount of papers has called for automatic measures to find the desired relevant publications, so-called paper recommendation systems. As the number of publications increases so does the amount of paper recommendation systems. Former literature reviews focused on discussing the general landscape of approaches throughout the years and highlight the main directions. We refrain from this perspective, instead we only consider a comparatively small time frame but analyse it fully. In this literature review we discuss used methods, datasets, evaluations and open challenges encountered in all works first released between January 2019 and October 2021. The goal of this survey is to provide a comprehensive and complete overview of current paper recommendation systems.

show abstract

“…ST_RippleNet not only introduces the KGE method into recommendations but also mines potential user information. In this paper, when ST_RippleNet is applied to music, books and movies, it is found that compared with the current mainstream recommendation [19] methods, ST_RippleNet achieves AUC gains of 6.4% to 37.4%, 0.8% to 18.4% and 0.4% to 41.2%, respectively, and ACC gains of 7.6% to 31.9%, 4.7% to 24.2% and 0.9% to 44.7%.…”

Section: Introductionmentioning

confidence: 96%

Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

Yang¹,

Cheng²

2021

IJCIS

View full text Add to dashboard Cite

In recommendation algorithms, data sparsity and cold start problems are inevitable. To solve such problems, researchers apply auxiliary information to recommendation algorithms, mine users' historical records to obtain more potential information, and then improve recommendation performance. In this paper, ST_RippleNet, a model that combines knowledge graphs with deep learning, is proposed. This model starts by building the required knowledge graph. Then, the potential interest of users is mined through the knowledge graph, which stimulates the spread of users' preferences on the set of knowledge entities. In preference propagation, we use a triple multi-layer attention mechanism to obtain triple information through the knowledge graph and use the user preference distribution for candidate items formed by users' historical click information to predict the final click probability. Using ST_RippleNet model can better obtain triple information in knowledge graph and mine more useful information. In the ST_RippleNet model, the music data set is added to the movie and book data set; additionally, an improved loss function is used in the model, which is optimized by the RMSProp optimizer. Finally, the tanh function is added to predict the click probability to improve recommendation performance. Compared with current mainstream recommendation methods, ST_RippleNet achieves very good performance in terms of the area under the curve (AUC) and accuracy (ACC) and substantially improves movie, book and music recommendations.

show abstract

Paper recommendation based on heterogeneous network embedding

Cited by 60 publications

References 32 publications

Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis

Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis

Scientific paper recommendation systems: a literature review of recent publications

Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

Contact Info

Product

Resources

About