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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Deng, Yutian Xiao, Dajun Yu, Fang Zhang, Hong China Electric Power Research Institute Nanjing China Central China Branch of State Grid Corporation of China Wuhan China

ISBN: (纸本)9798350333664

In the field of power dispatching, more and more tasks have adopted artificial intelligence solutions, and related research and literature are also showing an exponential explosive growth. In order to solve the problem of artificial intelligence information overload in power dispatching scenario, and to facilitate researchers with experience in power dispatching but lacking experience in artificial intelligence to use related algorithm models more effectively and conveniently in their work, this paper constructs a domain knowledge graph of artificial intelligence models for power dispatching scenario, and proposes an artificial intelligence model recommendation method based on knowledge graph and scene label matching. According to the cosine similarity of scene labels, the artificial intelligence model mapped on the knowledge graph is recommended. This method can be applied to the actual power dispatching scenario to provide personalized artificial intelligence model recommendation for different scientific research tasks, which greatly improves the retrieval efficiency of relevant researchers. © 2023 IEEE.

关键词： artificial intelligence model knowledge graph power dispatching recommendation algorithm scene label

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A conditional random field recommendation method based on tripartite graph

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期238卷

作者： Wang, Xin Han, Lixin Li, Jingxian Yan, Hong Hohai Univ Sch Comp & Informat Nanjing 211106 Peoples R China Jinling Inst Technol Sch Software Engn Nanjing 211169 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

Recommender System (RS) has generated widespread attention with the aim of expanding different items. Among graph-based recommendation methods, the tripartite graph can better manage data sparsity and cold start, while improving the metrics of various recommendations such as recall, precision, and diversity. Existing tripartite graph-based methods encounter numerous challenges, including mitigating data sparsity, improving diversity, and capturing potential user preferences via social relations. To address these challenges, a Conditional Random Field based on Tripartite Graph (CRF-TG) is proposed. The tripartite graph consists of the user, item, and trust level. The method can mine potentially similar users, create probabilistic models based on TG, and uncover potential user preferences. Moreover, to mine the users with similar preferences outside the social relationship, the random walk method is used to test CRF-TG. Experiments are designed to verify the validity of CRF-TG. Compared to the others considered methods, CRF-TG gives a 15% increase on average in performance indicators such as diversity, recall, and F1.

关键词： recommendation algorithm Graph-based recommendation Conditional random field Data sparsity Tripartite graph Diversity

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

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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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Deep recommendation Model Combining Long-and Short-Term Interest Preferences

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IEEE ACCESS 2021年 9卷 166455-166464页

作者： Niu, Lushuai Peng, Yan Liu, Yimao Sichuan Univ Sci & Engn Coll Automat & Informat Engn Yibin Peoples R China

The existing sequential recommendation algorithms cannot effectively capture and solve the problems such as the dynamic preferences of users over time. This paper proposes a deep recommendation model CLSR (Combines Long-term and Short-term interest recommendation) that Combines long-term and short-term interest preferences. Firstly, the model models the potential feature representation of users and items, and uses the self-attention mechanism to capture the relationship between items in the interaction of users' historical behavior, so as to better learn the short-term interest representation of users. At the same time, the BiGRU network is used to extract the features of users' long-term interests on a deep level. Finally, the features of long-term and short-term interest are fused. On four publicly available datasets, experimental results show that the proposed method has better improvement on HR@N, NDCG@N and MRR@N, which validates the effectiveness of the model.

关键词： Mathematical models Logic gates Data mining Data models Predictive models Licenses Context modeling recommendation algorithm self-attention mechanism bidirectional gating cyclic network sequence recommendation deep learning

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融合多粒度社团特征与快速并行矩阵分解的复杂网络个性化推荐算法

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运筹与模糊学 2025年第2期15卷 443-459页

作者：李贵平艾均苏湛上海理工大学光电信息与计算机工程学院上海

针对复杂网络环境下个性化推荐系统面临的数据稀疏性、计算效率及用户偏好动态性等挑战，本文提出了一种融合多粒度社团特征与快速并行矩阵分解的推荐算法。该算法首先构建基于用户相似度的复杂网络，并引入改进的社团检测算法实现多粒... 详细信息

针对复杂网络环境下个性化推荐系统面临的数据稀疏性、计算效率及用户偏好动态性等挑战，本文提出了一种融合多粒度社团特征与快速并行矩阵分解的推荐算法。该算法首先构建基于用户相似度的复杂网络，并引入改进的社团检测算法实现多粒度社团结构划分，生成粗细粒度不同的用户群体；随后，设计了一种基于用户活跃度的自适应层次选择机制，旨在根据用户行为特征动态选择合适的社团层次进行推荐，以缓解数据稀疏性问题并提升推荐精度。在此基础上，本文提出一种基于多层用户相似性网络的快速并行梯度下降矩阵分解模型，有效捕捉用户在不同粒度上的偏好特征，并结合用户全局偏好信息与局部社团特征。多个真实数据集上的实验结果表明，相较于传统推荐算法，融合多粒度社团特征与快速并行矩阵分解算法在准确性、精确率和召回率等指标上均有不同程度的提升，验证了该算法在提高推荐质量和可扩展性方面的有效性。

关键词：用户相似性多粒度层次划分复杂网络推荐算法并行梯度下降矩阵分解社团检测 User Similarity Multi-Granularity Hierarchical Partitioning Complex Network recommendation algorithm Parallel Gradient Descent Matrix Factorization Community Detection

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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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