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Application of the algorithm of separating graph neural recommendation model in health information system

JOURNAL OF DECISION SYSTEMS 2021年

作者： Huang, Jie Hunan Prov Engn Res Ctr Aircraft Maintenance Changsha 410124 Hunan Peoples R China Changsha Aeronaut Vocat & Tech Coll Changsha Peoples R China

In order to solve the problem of sparse and unbalanced data in recommendation system, a separable graph neural recommendation model (SGNR) based on graph information aggregation was proposed. It models the group characteristics of users and objects and extracts the collaborative characteristics of the group, which can effectively alleviate the problem of data sparsity and imbalance. In order to solve the problem that heterogeneous information network is difficult to construct, in the process of constructing graph neural network, the process of graph information collection is separated from the nonlinear transformation process of neural network, so that the modelling process is simpler. The experimental data is based on the data set of employee health information system, and the algorithm is verified and analysed by comparing various recommendation models. Experiments show that the SGNR method improves the efficiency and availability of the model, and the algorithm performance is greatly improved.

关键词： recommendation algorithm MAE RMSE Bayesian personalised ranking algorithm RNNs

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Improved Hybrid Collaborative Fitering algorithm Based on Spark Platform

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Wuhan University Journal of Natural Sciences 2023年第5期28卷 451-460页

作者： YOU Zhen HU Hongwen WANG Yutao XUE Jinyun YI Xinwu College of Computer Information Engineering Jiangxi Normal UniversityNanchang 330022JiangxiChina National-Level International Science and Technology Cooperation Base of Networked Supporting Software Nanchang 330022JiangxiChina

An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering *** core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model(LFM)and the Improved Item Clustering and Similarity Calculation Collaborative Filtering algorithm(ITCSCF).To begin with,the items are clustered based on their attribute dimension,which accelerates the computation of the nearest neighbor ***,H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular *** improvement enhances the rationality of scoring similarity and reduces the impact of data ***,a weighting function is employed to combine the various improved *** balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation *** address the real-time and scalability concerns,the algorithm leverages the Spark big data distributed cluster computing *** were conducted using the public dataset Movie Lens,where the improved algorithm’s performance was compared against the algorithm before enhancement and the algorithm running on a single *** experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity,recommendation personalization,accuracy,recall,and efficiency.

关键词： recommendation algorithm collaborative filtering latent factor model score weighting item clustering spark similarity calculation

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Multi-head Self-attention recommendation Model based on Feature Interaction Enhancement

Multi-head Self-attention Recommendation Model based on Feat...

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IEEE International Conference on Communications (ICC)

作者： Yin, Yunfei Huang, Caihao Sun, Jingqin Huang, Faliang Chongqing Univ Coll Comp Sci Chongqing Peoples R China Nanning Normal Univ Sch Comp & Informat Engn Nanning Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

In the recommendation system, click-through rate (CTR) prediction is a popular research direction. Aiming at the problem of excessive compression of features in Factorization Machine (FM) and its variant models, a recommendation model that combines feature interaction enhancement and multi-head self-attention is proposed. Hadamard product, feature vector splicing and multi-layer perception network methods are used for low-level feature vector interactive processing in this paper, and multi-head self-attention mechanism and residual network model for high-level feature interactive processing are used. By designing the fusion mechanism, the parallel low-order feature interaction network and the high-order feature interaction network are merged. The experimental results on the four benchmark data sets show that the multi-head self-attention model based on high-order feature interaction enhancement proposed in this paper outperforms existing models in terms of click-through rate prediction accuracy.

关键词： recommendation algorithm click-through rate prediction deep Learning feature interaction

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Analysis of Xinhua’s Cross-Platform Discourse on WeChat Public Media Under the Background of Big Data

Analysis of Xinhua’s Cross-Platform Discourse on WeChat Pub...

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作者： Sun, Yike Yu, Tianxin Shridhar, V. Advertising Institute Communication University of China Beijing China Academy of Literature Guangxi Minzu University Nanning China Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

With the development of media technology, the audience’s news contact habits have changed. Media integration is an important measure to respond to the shift in digital news dissemination. Cross-platform dissemination of news discourse is an important content of mainstream media communication practice on social media platforms. Previous research on media integration has focused on the integration of media channels and digital technology, with less in-depth exploration of the language characteristics of the two media at the micro-discourse level. Based on the background data of Xinhua News Agency's official account, this paper analyzes the language characteristics of the title of Xinhua News Agency’s WeChat official account, with a view to explaining the relationship between Xinhua News Agency’s discourse characteristics and communication effects from a linguistic perspective, providing a new perspective for theoretical research and theoretical support for business practice. The research is conducted under the control dialectical relationship theory and believes that there are three parties involved in the WeChat news discourse of Xinhua News Agency: national will, network subculture, and commercial interests. Xinhua News Agency still holds the dominant position in promoting online value. Xinhua News Agency uses online discourse technology to express the will of the country. algorithm visibility is the main factor affecting the discourse technology and dissemination path of Xinhua News Agency’s WeChat news. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Critical discourse Dialectical theory of control recommendation algorithm Social media

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The filter bubble generated by artificial intelligence algorithms and the network dynamics of collective polarization on YouTube: the case of South Korea

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ASIAN JOURNAL OF COMMUNICATION 2024年第2期34卷 195-212页

作者： Park, Han Woo Park, Sejung YeungNam Univ Dept Media & Commun Interdisciplinary Grad Programs Digital Convergenc Gyongsan South Korea Pukyong Natl Univ Div Global & Interdisciplinary Studies Busan South Korea Pukyong Natl Univ Div Global & Interdisciplinary Studies 45 Yongso ro Busan 48513 South Korea

This study examined the role of the 'filter bubble,' an algorithm-mediated YouTube video suggestion system, in political polarization and the presence of echo chamber patterns in public engagement. We examined the mechanism by which automated YouTube recommendations augment selective exposure to ideologically similar content and the network-based dynamics of collective polarization. We collected lists of videos recommended by conservative and progressive news media and the accompanying replies using YouTube application programming interfaces (APIs) embedded in YouTube Data Tools and Webometric Analyst 2.0. The study examined similarities in content of related videos and conversation networks between commenters, as well as the relationships between the videos, channels, and commenters. Almost half of the videos had content similar to the original videos offered by both conservative and progressive news media. The filter bubbles in progressive media were not as strong as those in conservative media. More than 30% of the videos recommended by progressive media were from legacy media, while this figure was 25% for conservative sources. This study has significant implications for reverse engineering studies because it collected data through APIs, which are neutral and can access YouTube servers without logging into individual IDs.

关键词： Automated recommendation filter bubble network analysis recommendation algorithm South Korea YouTube

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Analyzing User Engagement with TikTok's Short Format Video recommendations using Data Donations 24

Analyzing User Engagement with TikTok's Short Format Video R...

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ACM CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Zannettou, Savvas Nemes-Nemeth, Olivia Ayalon, Oshrat Goetzen, Angelica Gummadi, Krishna P. Redmiles, Elissa M. Roesner, Franziska Delft Univ Technol Delft Netherlands Max Planck Inst Informat Saarbrucken Germany Univ Haifa Haifa Israel Georgetown Univ Washington DC USA Univ Washington Seattle WA USA

ISBN: (纸本)9798400703300

Short-format videos have exploded on platforms like TikTok, Instagram, and YouTube. Despite this, the research community lacks large-scale empirical studies into how people engage with short-format videos and the role of recommendation systems that offer endless streams of such content. In this work, we analyze user engagement on TikTok using data we collect via a data donation system that allows TikTok users to donate their data. We recruited 347 TikTok users and collected 9.2M TikTok video recommendations they received. By analyzing user engagement, we find that the average daily usage time increases over the users' lifetime while the user attention remains stable at around 45%. We also find that users like more videos uploaded by people they follow than those recommended by people they do not follow. Our study offers valuable insights into how users engage with short-format videos on TikTok and lessons learned from designing a data donation system.

关键词： TikTok recommendation algorithm User Engagement Data Donation

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Personalized scientific and technological literature resources recommendation based on deep learning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 2981-2996页

作者： Zhang, Jin Gu, Fu Ji, Yangjian Guo, Jianfeng Zhejiang Univ Dept Ind & Syst Engn Hangzhou Peoples R China Zhejiang Univ Ctr Engn Management Polytech Inst Hangzhou Peoples R China Zhejiang Univ Natl Inst Innovat Management Hangzhou Peoples R China Chinese Acad Sci Inst Sci Beijing Peoples R China Chinese Acad Sci Inst Dev Beijing Peoples R China Univ Chinese Acad Sci Sch Publ Policy & Management Beijing Peoples R China

To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers' titles and abstracts. Secondly, an academic literature topic model is built using term frequency-inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers' preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress.

关键词： Scientific and technological literature resources personalized recommendation deep learning recommendation algorithm

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Code Reviewer recommendation in Tencent: Practice, Challenge, and Direction 44

Code Reviewer Recommendation in Tencent: Practice, Challenge...

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ACM/IEEE 44th International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Chen, Qiuyuan Kong, Dezhen Bao, Lingfeng Sun, Chenxing Xia, Xin Li, Shanping Zhejiang Univ Hangzhou Zhejiang Peoples R China Tencent Technol Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665495905

Code review is essential for assuring system quality in software engineering. Over decades in practice, code review has evolved to be a lightweight tool-based process focusing on code change: the smallest unit of the development cycle, and we refer to it as Modern Code Review (MCR). MCR involves code contributors committing code changes and code reviewers reviewing the assigned code changes. Such a reviewer assigning process is challenged by efficiently finding appropriate reviewers. Recent studies propose automated code reviewer recommendation (CRR) approaches to resolve such challenges. These approaches are often evaluated on open-source projects and obtain promising performance. However, the code reviewer recommendation systems are not widely used on proprietary projects, and most current reviewer selecting practice is still manual or, at best, semi-manual. No previous work systematically evaluated these approaches' effectiveness and compared each other on proprietary projects in practice. In this paper, we performed a quantitative analysis of typical recommendation approaches on proprietary projects in Tencent. The results show an imperfect performance of these approaches on proprietary projects and reveal practical challenges like the "cold start problem". To better understand practical challenges, we interviewed practitioners about the expectations of applying reviewer recommendations to a production environment. The interview involves the current systems' limitations, expected application scenario, and information requirements. Finally, we discuss the implications and the direction of practical code reviewer recommendation tools.

关键词： code review code reviewer recommendation recommendation algorithm

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Research on Adaptive QoE Global Bandwidth Allocation algorithm Based on Enhanced Greed

Research on Adaptive QoE Global Bandwidth Allocation Algorit...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Li, Shufeng Li, Tianchi Liu, Jianzhang Commun Univ China Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350378412

With the rapid development of the Internet and the improvement of computing and storage capabilities, ultra-high-definition technology has been rapidly applied in various fields. In the current domain of streaming media broadcasting, providing the necessary network bandwidth for ultra-high-definition cloud performing arts services has become a critical issue. Despite the establishment of Quality of Experience evaluation models, bandwidth allocation algorithms based on QoE still face challenges such as accuracy, user satisfaction, and low bandwidth utilization. In response to these issues, this paper proposes an Adaptive QoE Global Bandwidth Allocation algorithm Based on Enhanced Greed. Building upon existing bandwidth allocation algorithms, this proposed algorithm incorporates greedy algorithms and enhances the profit calculation method and execution strategy of the greedy algorithm to maximize the satisfaction rate of user QoE demands and bandwidth utilization. Simulation results demonstrate the outstanding performance of the proposed algorithm in terms of bandwidth allocation accuracy, user QoE satisfaction rate, and bandwidth utilization.

关键词： UHD cloud performing arts live quality of experience recommendation algorithm bandwidth allocation

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A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

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JOURNAL OF INFORMATION SCIENCE 2022年第6期48卷 749-766页

作者： Wang, Yong Zhao, Xuhui Zhang, Zhiqiang Zhang, Leo Yu Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China Deakin Univ Geelong Vic Australia

The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.

关键词： Collaborative filtering Hellinger distance recommendation algorithm similarity measure sparse data

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