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Hierarchical Social Recommendation Model Based on a Graph Neural Network

Zhongqin Bi, Lina Jing, Meijing Shan, Shuming Dou, Shiyang Wang, Xiaoxian Yang
2021 Wireless Communications and Mobile Computing  
In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling.  ...  Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation.  ...  Acknowledgments This work is supported by the National Nature Science Foundation of China (No. 61972357).  ... 
doi:10.1155/2021/9107718 fatcat:s4ovvdloyjgfdi7wwvvytfbox4

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation [article]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
2021 arXiv   pre-print
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.  ...  ; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions.  ...  DiffNet is designed to simulate how users are influenced by the recursive social diffusion process for social recommendation with the social GNN modeling.  ... 
arXiv:2104.13030v3 fatcat:7bzwaxcarrgbhe36teik2rhl6e

Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications

Jia Wu, Shirui Pan, Chuan Zhou, Gang Li, Wu He, Chengqi Zhang
2018 Complexity  
The model can extract features automatically during the learning process without any prior knowledge or handgenerated features for segmentation.  ...  Social Network Data Social influence analysis is important for many social network applications, including recommendation and cyber security analysis.  ...  Acknowledgments The Guest Editorial Team would like to express their gratitude to all the authors for their interest in selecting this special issue as a venue for their scholarly work dissemination.  ... 
doi:10.1155/2018/7861860 fatcat:6mc7cqtqzjcjlghqbmlhb5hifa

Towards Fashion Recommendation: An AI System for Clothing Data Retrieval and Analysis [chapter]

Maria Th. Kotouza, Sotirios–Filippos Tsarouchis, Alexandros-Charalampos Kyprianidis, Antonios C. Chrysopoulos, Pericles A. Mitkas
2020 IFIP Advances in Information and Communication Technology  
In our use case scenario, datasets of garment products are retrieved from two different sources and are transformed into a specific format by making use of Natural Language Processes.  ...  datasets are clustered separately using different mixed-type clustering algorithms and comparative results are provided, highlighting the usefulness of the clustering procedure in the clothing product recommendation  ...  This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation  ... 
doi:10.1007/978-3-030-49186-4_36 fatcat:jwaxo2argnhbrix2ojdpcyq22y

Research Commentary on Recommendations with Side Information: A Survey and Research Directions [article]

Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke
2019 arXiv   pre-print
One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models.  ...  features).  ...  Jie Zhang by the BMW Tech Office Singapore.  ... 
arXiv:1909.12807v2 fatcat:2nj4crzcd5attidhd3kneszmki

A multi-resolution approach to learning with overlapping communities

Lei Tang, Xufei Wang, Huan Liu, Lei Wang
2010 Proceedings of the First Workshop on Social Media Analytics - SOMA '10  
The recent few years have witnessed a rapid surge of par-ticipatory web and social media, enabling a new laboratory for studying human relations and collective behavior on an unprecedented scale.  ...  social connections to determine the preferences or behaviors of individuals such as whether a user supports a certain political view, whether one likes one product, whether he/she would like to vote for  ...  For effective classification learning, we regularize node affiliations by normalizing each instance's social dimensions to sum up to 1.  ... 
doi:10.1145/1964858.1964861 dblp:conf/kdd/TangWLW10 fatcat:zjyna7aj4fdyzefbiobt52bouy

Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

Alpamis Kutlimuratov, Akmalbek Abdusalomov, Taeg Keun Whangbo
2020 Symmetry  
them by regularizing the process with tag information as an auxiliary parameter.  ...  Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two  ...  Moreover, the authors would like to thank the editor and anonymous referees for the constructive comments in improving the contents and presentation of this paper.  ... 
doi:10.3390/sym12111930 fatcat:7acspi3ipnedlmfb5fmm43ddji

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball [article]

Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates
2021 arXiv   pre-print
To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.  ...  Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability.  ...  By contrast, regularization-based methods devise additional loss terms that capture the KG structure and use these to regularize the recommender model learning.  ... 
arXiv:2101.04852v2 fatcat:lipdisr26jcqna3nfipdytfbw4

MOCA: Multi-Objective, Collaborative, and Attentive Sentiment Analysis

Jia-Dong Zhang, Chi-Yin Chow
2019 IEEE Access  
., reviews) written by users for items. Most current works model this problem as a supervised learning task, i.e., classification or regression.  ...  Recent studies argue that user preferences and item characteristics also have significant influences on ratings that are modeled by learning user-item-specific text embeddings based on neural networks.  ...  [25] present recursive neural networks to learn sentence embeddings.  ... 
doi:10.1109/access.2019.2891019 fatcat:vlnbnoqpvzbtfduv4x2hv3r4ba

Parsing Natural Scenes and Natural Language with Recursive Neural Networks

Richard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, Christopher D. Manning
2011 International Conference on Machine Learning  
The features from the image parse tree outperform Gist descriptors for scene classification by 4%.  ...  The same algorithm can be used both to provide a competitive syntactic parser for natural language sentences from the Penn Treebank and to outperform alternative approaches for semantic scene segmentation  ...  We would like to thank Tianshi Gao for helping us with the feature computation, as well as Jiquan Ngiam and Quoc Le for many helpful comments.  ... 
dblp:conf/icml/SocherLNM11 fatcat:zdgn7oquirebtme3cml7vsni4a

Recent Advances in Heterogeneous Relation Learning for Recommendation [article]

Chao Huang
2021 arXiv   pre-print
Finally, we present an exploratory outlook to highlight several promising directions and opportunities in heterogeneous relational learning frameworks for recommendation.  ...  We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information.  ...  (GCN),DiffNet [Wu et al., 2019b] simulates the influence process between users, by recursively propagating information over the social network for user and item representation refinement.  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [article]

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, Liefeng Bo
2021 arXiv   pre-print
Accurate user and item embedding learning is crucial for modern recommender systems.  ...  Many practical recommendation scenarios involve multi-typed user interactive behaviors (e.g., page view, add-to-favorite and purchase), which presents unique challenges that cannot be handled by current  ...  Acknowledgments We thank the anonymous reviewers for their constructive feedback and comments.  ... 
arXiv:2110.04000v1 fatcat:44xhyegydzbmzlf5ytlznzhrqm

Recursive Neural Networks Based on PSO for Image Parsing

Guo-Rong Cai, Shui-Li Chen
2013 Abstract and Applied Analysis  
State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters.  ...  This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs).  ...  As for RNN, Socher recommends that the size of the hidden layer 𝑛 = 100, the penalization term for incorrect parsing decisions 𝜅 = 0.05, and the regularization parameter 𝜆 = 0.001.  ... 
doi:10.1155/2013/617618 fatcat:m7ks5yv3ufftzah3oirhz7eqay

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Rongmei Zhao, Xi Xiong, Xia Zu, Shenggen Ju, Zhongzhi Li, Binyong Li
2020 Complexity  
First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical  ...  We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process.  ...  (v) DiffNet: It is designed to simulate how users are influenced by the recursive social diffusion process for social recommendation. is method considers the heterogeneity of social networks, which separately  ... 
doi:10.1155/2020/9071624 fatcat:akl2qqzoyrbhbogjhhx7gpbl5a

Neural Tree Indexers for Text Understanding

Tsendsuren Munkhdalai, Hong Yu
2017 Association for Computational Linguistics (ACL). Annual Meeting Conference Proceedings  
However, the current recursive architecture is limited by its dependence on syntactic tree.  ...  In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language.  ...  This work was supported in part by the grant HL125089 from the National Institutes of Health (NIH).  ... 
pmid:29081577 pmcid:PMC5657441 fatcat:lbfx6koqhjbxnd4qp7zmleachy
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