Temporal sensitive heterogeneous graph neural network for news recommendation

Ji, Zhenyan; Wu, Mengdan; Yang, Hong; Íñigo, José Enrique Armendáriz

doi:10.1016/j.future.2021.06.007

Cited by 23 publications

(8 citation statements)

References 35 publications

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“…Recently, to take into account the impact of users' interest, deep learning-based models have been proposed to model the complex user-news interactions, and capture the dynamic properties of news and users. 3,8,[20][21][22][23][24][25][26][27][28] For example, Okura et al 3 proposed to learn the user representation from the news viewed by the user through gate recurrent unit (GRU) and study the news representation from the news content through the automatic encoder. Ji et al 20 designed the news feature extractor temporal-dimensional attention convolutional neural network to learn news representation and proposed an improved sequence information extraction model, Rein-LSTM, to extract the user-click sequence feature.…”

Section: Related Researchmentioning

confidence: 99%

“…3,8,[20][21][22][23][24][25][26][27][28] For example, Okura et al 3 proposed to learn the user representation from the news viewed by the user through gate recurrent unit (GRU) and study the news representation from the news content through the automatic encoder. Ji et al 20 designed the news feature extractor temporal-dimensional attention convolutional neural network to learn news representation and proposed an improved sequence information extraction model, Rein-LSTM, to extract the user-click sequence feature. Cho et al 24 proposed a simple contextual deep-learning module that can be attached to content-based solutions in a modular manner.…”

Section: Related Researchmentioning

confidence: 99%

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Graph neural network news recommendation based on weight learning and preference decomposition

Lu,

2023

J. Electron. Imag.

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Personalized news suggestions are an important technology to enhance people's online news reading experiences. How to better understand users and news representation is a major issue in news recommendation. The majority of cutting-edge news recommendation techniques mostly neglect the link between title and content, explicitly and implicitly. They neglect to take into account the effects of many prospective news preferences on people's behavior when they click on various news items. We first build a user-news interaction graph and then present the weight learning and preference decomposition (WLPD) news recommendation model for graph neural networks, which is based on WLPD. This model not only takes into account the impact of the relationship between news titles and content, explicit and implicit, on the likelihood that users will click on the news, but also takes into account the various potential preferences between users and news interaction. Finally, using actual news databases, we run a number of experiments. We discover that our model significantly improved in terms of accuracy and performance compared with other cutting-edge news recommendation techniques.

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Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Graph neural network news recommendation based on weight learning and preference decomposition

Lu,

2023

J. Electron. Imag.

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“…In formula (5), δ i , δ j are the community number of the node, and the adjacency matrix element of the network is represented as a ij , the edge in the network graph' number is represented as χ, and the node order is represented as β i , β j .…”

Section: Community Detecting Algorithmmentioning

confidence: 99%

“…News recommendation as a means of news ltering and user positioning. It can recommend news topics that may be of interest to users based on their historical reading habits, ensure that users can quickly and e ectively obtain the data information they need, reduce the cost of reading, avoid the occurrence of information overload, and provide high-quality and personalized services to users [4][5][6]. At present, in the process of developing e-commerce activities, the recommendation system under the condition of information overload will be applied, while there are few personalized recommendation systems for news.…”

Section: Introductionmentioning

confidence: 99%

Personalized News Recommendation Algorithm for Event Network

Han

2022

Mathematical Problems in Engineering

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In order to improve the level of personalized news recommendation efficiently and accurately, an event network-oriented personalized news recommendation algorithm is proposed. First, the event network is used to analyze, predict users’ interests and preferences, and actively push information content to meet users’ personalized needs, so as to build a personalized news recommendation model. Under the mobile Internet technology, combined with the characteristics of the Internet, through the position and title similarity of sentences in the document and other features, the combined features are formed to calculate the sentence weight. Finally, the sentences are extracted according to the weight ranking to generate the news summary, so as to realize the research on personalized news recommendation algorithm for event network. The experimental results show that the proposed algorithm has high recall and coverage, short time, good recommendation effect, and strong recommendation performance.

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“…Figure 1 depicts the PRISMA [79] workflow for this study. We refrain from including works in our study which do not identify as scientific paper recommendation systems such as Wikipedia article recommendation [70,78,85] or general news article recommendation [33,43,103]. Citation recommendation systems [72,90,124] are also out of scope of this literature review.…”

Section: Scopementioning

confidence: 99%

Scientific paper recommendation systems: a literature review of recent publications

Kreutz

Schenkel

2022

Int J Digit Libr

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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.

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Temporal sensitive heterogeneous graph neural network for news recommendation

Cited by 23 publications

References 35 publications

Graph neural network news recommendation based on weight learning and preference decomposition

Graph neural network news recommendation based on weight learning and preference decomposition

Personalized News Recommendation Algorithm for Event Network

Scientific paper recommendation systems: a literature review of recent publications

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