FrankBlood/RSPapers

Must-read papers on Recommender System.

Must-read papers on Recommender System

This repository provides a list of papers including comprehensive surveys, classical recommender system, social recommender system, deep learing-based recommender system, cold start problem in recommender system, hashing for recommender system, exploration and exploitation problem, explainability in recommender system as well as click through rate prediction for recommender system.

[New!] Add the new part of Knowledge Graph for RS.

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01-Surveys: a set of comprehensive surveys about recommender system, such as hybrid recommender systems, social recommender systems, poi recommender systems, deep-learning based recommonder systems and so on.

02-Classical RS: a set of famous recommendation papers which make predictions with some classic models and practical theory.

03-Social RS: several papers which utilize trust/social information in order to alleviate the sparsity of ratings data.

04-Deep Learning-based RS: a set of papers to build a recommender system with deep learning techniques.

05-Cold Start Problem in RS: some papers specifically dealing with the cold start problems inherent in collaborative filtering.

06-POI RS: it focus on helping users explore attractive locations with the information of location-based social networks.

07-Hashing for RS: some hashing techniques for recommender system in order to training and making recommendation efficiently.

08-EE Problem in RS: some articles about exploration and exploitation problems in recommendation.

09-Explainability on RS: it focus on addressing the problem of 'why', they not only provide the user with the recommendations, but also make the user aware why such items are recommended by generating recommendation explanations.

10-CTR Prediction for RS: as one part of recommendation, click-through rate prediction focuses on the elaboration of candidate sets for recommendation.

11-Knowledge Graph for RS: knowledge graph, as the side information of behavior interaction matrix in recent years, which can effectively alleviate the problem of data sparsity and cold start, and can provide a reliable explanation for recommendation results.

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*All papers are sorted by year for clarity.

Surveys

• Burke et al. Hybrid Recommender Systems: Survey and Experiments. USER MODEL USER-ADAP, 2002.

• Adomavicius et al. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 2005.

• Su et al. A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009.

• Cacheda et al. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM TWEB, 2011.

• Zhang et al. Tag-aware recommender systems: a state-of-the-art survey. J COMPUT SCI TECHNOL, 2011.

• Tang et al. Social recommendation: a review. SNAM, 2013.

• Yang et al. A survey of collaborative filtering based social recommender systems. COMPUT COMMUN, 2014.

• Shi et al. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM COMPUT SURV, 2014.

• Chen et al. Recommender systems based on user reviews: the state of the art. USER MODEL USER-ADAP, 2015.

• Xu et al. Social networking meets recommender systems: survey. Int.J.Social Network Mining, 2015.

• Yu et al. A survey of point-of-interest recommendation in location-based social networks. In Workshops at AAAI, 2015.

• Efthalia et al. Parallel and Distributed Collaborative Filtering: A Survey. Comput. Surv., 2016.

• Singhal et al. Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works. arXiv, 2017.

• Muhammad et al. Cross Domain Recommender Systems: A Systematic Literature Review. ACM Comput. Surv, 2017.

• Massimo et al. Sequence-Aware Recommender Systems. ACM Comput. Surv, 2018.

• Zhang et al. Deep learning based recommender system: A survey and new perspectives. ACM Comput.Surv, 2018.

• Batmaz et al. A review on deep learning for recommender systems: challenges and remedies. Artificial Intelligence Review, 2018.

• Zhang et al. Explainable Recommendation: A Survey and New Perspectives. arXiv, 2018.

• Liu et al. Survey of matrix factorization based recommendation methods by integrating social information. Journal of Software, 2018.

• Shoujin et al. A Survey on Session-based Recommender Systems. arXiv, 2019.

• Shoujin et al. Sequential Recommender Systems: Challenges, Progress and Prospects. IJCAI, 2019.

• Zhu et al. Research Commentary on Recommendations with Side Information: A Survey and Research Directions. Electron. Commer. Res. Appl., 2019.

• Dietmar et al. A Survey on Conversational Recommender Systems. arXiv, 2020.

• Qingyu et al. A Survey on Knowledge Graph-Based Recommender Systems. arXiv, 2020.

Classical Recommender System

• Goldberg et al. Using collaborative filtering to weave an information tapestry. COMMUN ACM, 1992.

• Resnick et al. GroupLens: an open architecture for collaborative filtering of netnews. CSCW, 1994.

• Sarwar et al. Application of dimensionality reduction in recommender system-a case study. 2000.

• Sarwar et al. Item-based collaborative filtering recommendation algorithms. WWW, 2001.

• Linden et al. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE INTERNET COMPUT, 2003.

• Lemire et al. Slope one predictors for online rating-based collaborative filtering. SDM, 2005.

• Zhou et al. Bipartite network projection and personal recommendation. Physical Review E, 2007.

• Mnih et al. Probabilistic matrix factorization. NIPS, 2008.

• Koren et al. Factorization meets the neighborhood: a multifaceted collaborative filtering model. SIGKDD, 2008.

• Pan et al. One-class collaborative filtering. ICDM, 2008.

• Hu et al. Collaborative filtering for implicit feedback datasets. ICDM, 2008.

• Weimer et al. Improving maximum margin matrix factorization. Machine Learning, 2008.

• Koren et al. Matrix factorization techniques for recommender systems. Computer, 2009.

• Agarwal et al. Regression-based latent factor models. SIGKDD, 2009.

• Koren et al. The bellkor solution to the netflix grand prize. Netflix prize documentation, 2009.

• Rendle et al. BPR: Bayesian personalized ranking from implicit feedback. UAI, 2009.

• Koren et al. Collaborative filtering with temporal dynamics. COMMUN ACM, 2010.

• Khoshneshin et al. Collaborative filtering via euclidean embedding. RecSys, 2010.

• Liu et al. Online evolutionary collaborative filtering Recsys. RecSys, 2010.

• Koren et al. Factor in the neighbors: Scalable and accurate collaborative filtering. TKDD, 2010.

• Chen et al. Feature-based matrix factorization. arXiv, 2011.

• Rendle. Learning recommender systems with adaptive regularization. WSDM, 2012.

• Zhong et al. Contextual collaborative filtering via hierarchical matrix factorization. SDM, 2012.

• Lee et al. Local low-rank matrix approximation. ICML, 2013.

• Kabbur et al. Fism: factored item similarity models for top-n recommender systems. KDD, 2013.

• Hu et al. Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. SIGIR, 2014.

• Hernández-Lobato et al. Probabilistic matrix factorization with non-random missing data. ICML, 2014.

• Shi et al. Semantic path based personalized recommendation on weighted heterogeneous information networks. CIKM, 2015.

• Grbovic et al. E-commerce in your inbox: Product recommendations at scale. KDD, 2015.

• Barkan et al. Item2vec: neural item embedding for collaborative filtering. Machine Learning for Signal Processing, 2016.

• Liang et al. Modeling user exposure in recommendation. WWW, 2016.

• He et al. Fast matrix factorization for online recommendation with implicit feedback. SIGIR, 2016.

• Hsieh et al. Collaborative metric learning. WWW, 2017.

• Gao et al. BiNE: Bipartite Network Embedding. SIGIR, 2018.

• Xiangnan et al. Adversarial Personalized Ranking for Recommendation. SIGIR, 2018.

• Zhang et al. Metric Factorization: Recommendation beyond Matrix Factorization. 2018.

• Lei et al. Spectral Collaborative Filtering. RecSys, 2018.

• Chen et al. Collaborative Similarity Embedding for Recommender Systems. arXiv, 2019.

• Chuan et al. Heterogeneous Information Network Embedding for Recommendation. TKDE, 2019.

• Xiang et al. Neural Graph Collaborative Filtering. SIGIR, 2019.

Social Recommender System

• Ma, Hao, et al. Sorec: social recommendation using probabilistic matrix factorization. CIKM, 2008.

• Jamali et al. Trustwalker: a random walk model for combining trust-based and item-based recommendation. SIGKDD, 2009.

• Ma et al. Learning to recommend with trust and distrust relationships. RecSys, 2009.

• Ma et al. Learning to recommend with social trust ensemble. SIGIR, 2009.

• Jamali et al. A matrix factorization technique with trust propagation for recommendation in social networks. RecSys, 2010.

• Ma, Hao, et al. Recommender systems with social regularization. WSDM, 2011.

• Ma, Hao et al. Learning to recommend with explicit and implicit social relations. ACM T INTEL SYST TEC, 2011.

• Ma, Hao. An experimental study on implicit social recommendation. SIGIR, 2013.

• Yang et al. Social collaborative filtering by trust. IJCAI, 2013.

• Jiliang et al. Exploiting Local and Global Social Context for Recommendation. IJCAI, 2013.

• Zhao et al. Leveraging social connections to improve personalized ranking for collaborative filtering. CIKM, 2014.

• Chen et al. Context-aware collaborative topic regression with social matrix factorization for recommender systems. AAAI, 2014.

• Guo et al. TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings. AAAI, 2015.

• Wang et al. Social recommendation with strong and weak ties. CIKM, 2016.

• Jiliang et al. Recommendation with Social Dimensions. AAAI, 2016.

• Li et al. Social recommendation using Euclidean embedding. IJCNN, 2017.

• Zhang et al. Collaborative User Network Embedding for Social Recommender Systems. SDM, 2017.

• Yang et al. Social collaborative filtering by trust. IEEE T PATTERN ANAL, 2017.

• Park et al. UniWalk: Explainable and Accurate Recommendation for Rating and Network Data. arXiv, 2017.

• Rafailidis et al. Learning to Rank with Trust and Distrust in Recommender Systems. RecSys, 2017.

• Xixi et al. Additive Co-Clustering with Social Influence for Recommendation. RecSys, 2017.

• Zhao et al. Collaborative Filtering with Social Local Models. ICDM, 2017.

• Wang et al. Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. AAAI, 2018.

• Wenqi et al. Deep Modeling of Social Relations for Recommendation. AAAI, 2018

• Xuying et al. Personalized Privacy-Preserving Social Recommendation. AAAI,2018.

• Wen et al. Network embedding based recommendation method in social networks. WWW Poster, 2018.

• Lin et al. Recommender Systems with Characterized Social Regularization. CIKM Short Paper, 2018.

• Yu et al. Adaptive implicit friends identification over heterogeneous network for social recommendation. CIKM, 2018.

• Honglei et al. Social Collaborative Filtering Ensemble. PRICAI, 2018.

• Wenqi et al. Graph Neural Networks for Social Recommendation. WWW, 2019.

• Song et al. Session-based Social Recommendation via Dynamic Graph Attention Networks. WSDM, 2019.

• Wenqi et al. Deep Social Collaborative Filtering. RecSys, 2019.

• Wenqi et al. Deep Adversarial Social Recommendation. IJCAI, 2019.

• Qitian et al. Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust. IJCAI, 2019.

• Wu et al. SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation. AAAI, 2019.

• Wu et al. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender System. WWW, 2019.

• Wu et al. A Neural Influence Diffusion Model for Social Recommendation. SIGIR, 2019.

• Yang et al. Modelling High-Order Social Relations for Item Recommendation. arXiv, 2020.

Deep Learning based Recommender System

• Salakhutdinov et al. Restricted Boltzmann machines for collaborative filtering. ICML, 2007.

• Wang et al. Collaborative deep learning for recommender systems. KDD, 2015.

• Sedhain et al. Autorec: Autoencoders meet collaborative filtering. WWW, 2015.

• Li et al. Deep collaborative filtering via marginalized denoising auto-encoder. CIKM, 2015.

• Hidasi et al. Session-based recommendations with recurrent neural networks. ICLR, 2016.

• Covington et al. Deep neural networks for youtube recommendations. RecSys, 2016.

• Cheng et al. Wide & deep learning for recommender systems. Workshop on RecSys, 2016.

• Zheng et al. A neural autoregressive approach to collaborative filtering. ICML, 2016.

• Wu et al. Collaborative denoising auto-encoders for top-n recommender systems. WSDM, 2016.

• Kim et al. Convolutional matrix factorization for document context-aware recommendation. RecSys, 2016.

• Tan et al. Improved recurrent neural networks for session-based recommendations. Workshop on Deep Learning for Recommender Systems, 2016.

• Lian et al. CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. WWW, 2017.

• He et al. Neural collaborative filtering. WWW, 2017.

• Zheng et al. Joint deep modeling of users and items using reviews for recommendation. WSDM, 2017.

• Zhao et al. Leveraging Long and Short-term Information in Content-aware Movie Recommendation. arXiv, 2017.

• Li et al. Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback. arXiv, 2017.

• Xue et al. Deep Matrix Factorization Models for Recommender Systems. IJCAI, 2017. code

• Zhao et al. Learning and Transferring IDs Representation in E-commerce. KDD, 2018.

• Liang et al. Variational Autoencoders for Collaborative Filtering. WWW, 2018.

• Ebesu et al. Collaborative Memory Network for Recommendation Systems. SIGIR, 2018.

• Lian et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. KDD, 2018.

• Zhang et al. Next Item Recommendation with Self-Attention. 2018.

• Li et al. Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors. KDD, 2018.

• Grbovic et al. Real-time Personalization using Embeddings for Search Ranking at Airbnb. KDD, 2018.

• Ying et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD, 2018.

• Hu et al. Leveraging meta-path based context for top-n recommendation with a neural co-attention model. KDD, 2018.

• Christakopoulou et al. Local Latent Space Models for Top-N Recommendation. KDD, 2018.

• Bhagat et al. Buy It Again: Modeling Repeat Purchase Recommendations. KDD, 2018.

• Wang et al. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. KDD, 2018.

• Tran et al. Regularizing Matrix Factorization with User and Item Embeddings for Recommendation. CIKM, 2018.

• Zhou et al. Micro behaviors: A new perspective in e-commerce recommender systems. WSDM, 2018.

• Chen et al. Sequential recommendation with user memory networks. WSDM, 2018.

• Beutel et al. Latent Cross: Making Use of Context in Recurrent Recommender Systems. WSDM, 2018.

• Tang et al. Personalized top-n sequential recommendation via convolutional sequence embedding. WSDM, 2018.

• Chae et al. CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks. CIKM, 2018.

• Wu et al. Session-based Recommendation with Graph Neural Networks. AAAI, 2019.

• Zhi-Hong et al. DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System. AAAI, 2019.

• Zeping et al. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation. IJCAI, 2019.

• Dong Xi et al. BPAM: Recommendation Based on BP Neural Network with Attention Mechanism. IJCAI, 2019.

• Xin et al. CFM: Convolutional Factorization Machines for Context-Aware Recommendation. IJCAI, 2019.

• Xiao Zhou et al. Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems. IJCAI, 2019.

• Junyang et al. Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty. IJCAI, 2019.

• Feng Yuan et al. DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns. IJCAI, 2019.

• Yanan et al. Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation. IJCAI, 2019.

• Jiani et al. STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. IJCAI, 2019.

• An et al. CosRec: 2D Convolutional Neural Networks for Sequential Recommendation. CIKM, 2019.

• Hongwei et al. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. WWW, 2019.

• Maurizio et al. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. RecSys, 2019.

• Xin et al. Cfm: Convolutional factorization machines for context-aware recommendation. IJCAI, 2019.

Cold Start Problem in Recommender System

• Schein et al. Methods and metrics for cold-start recommendations. SIGIR, 2002.

• Seung-Taek et al. Pairwise Preference Regression for Cold-start Recommendation. RecSys, 2009.

• Gantner et al. Learning attribute-to-feature mappings for cold-start recommendations. ICDM, 2010.

• Sedhain et al. Social collaborative filtering for cold-start recommendations. RecSys, 2014.

• Zhang et al. Addressing cold start in recommender systems: A semi-supervised co-training algorithm. SIGIR, 2014.

• Kula. Metadata embeddings for user and item cold-start recommendations. arXiv, 2015.

• Sedhain et al. Low-Rank Linear Cold-Start Recommendation from Social Data. AAAI. 2017.

• Man et al. Cross-domain recommendation: an embedding and mapping approach. IJCAI, 2017.

• Cohen et al. Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations. RecSys, 2017.

• Dureddy et al. Handling Cold-Start Collaborative Filtering with Reinforcement Learning. arXiv, 2018.

• Fu et al. Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems. AAAI, 2019.

• Li. From Zero-Shot Learning to Cold-Start Recommendation. AAAI, 2019

• Hoyeop. Estimating Personalized Preferences Through Meta-Learning for User Cold-Start Recommendation. KDD, 2019.

POI Recommender System

• Mao et al. Exploiting geographical influence for collaborative point-of-interest recommendation. SIGIR, 2011.

• Chen et al. Fused matrix factorization with geographical and social influence in location-based social networks. AAAI, 2012.

• Jia et al. iGSLR: personalized geo-social location recommen dation: a kernel density estimation approach. SIGSPA, 2013.

• Jia et al. Lore: exploiting sequential influence for location recommendations. SIGSPATIAL, 2014

• Jia et al. Geosoca: Exploiting geographical, social and cat egorical correlations for point-of-interest recommendations. SIGIR, 2015.

• Huayu et al. Point-of-Interest Recommendations:Learning Potential Check-ins from Friends. KDD, 2016.

• Jing et al. Category-aware next point-of-interest recommendation via listwise Bayesian personalized ranking. IJCAI, 2017.

• Jarana et al. A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation. CIKM, 2017.

• Huayu et al. Learning user's intrinsic and extrinsic interests for point-of-interest recommendation: a unified approach. IJCAI, 2017.

• Wei Liu et al. Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism. IJCAI, 2019.

Hashing for RS

• Karatzoglou et al. Collaborative filtering on a budget. AISTAT, 2010.

• Zhou et al. Learning binary codes for collaborative filtering. SIGKDD, 2012.

• Zhang et al. Preference preserving hashing for efficient recommendation. SIGIR, 2014.

• Zhang et al. Discrete collaborative filtering. SIGIR, 2016.

• Lian et al. Discrete Content-aware Matrix Factorization. SIGKDD, 2017.

• Han et al. Discrete Factorization Machines for Fast Feature-based Recommendatio. IJCAI, 2018.

• Guibing et al. Discrete Trust-aware Matrix Factorization for Fast Recommendation. IJCAI, 2019.

• Chenghao et al. Discrete Social Recommendation. AAAI, 2019.

• Defu et al. LightRec: a Memory and Search-Efficient Recommender System. WWW, 2020.

EE in RS

• Auer et al. Using confidence bounds for exploitation-exploration trade-offs. JMLR, 2002.

• Li et al. A contextual-bandit approach to personalized news article recommendation. WWW, 2010.

• Li et al. Exploitation and exploration in a performance based contextual advertising system. SIGKDD, 2010.

• Chapelle et al. An empirical evaluation of thompson sampling. NIPS, 2011.

• Féraud et al. Random forest for the contextual bandit problem. Artificial Intelligence and Statistics. 2016.

• Li et al. Collaborative filtering bandits. SIGIR, 2016.

• Wang et al. Factorization Bandits for Interactive Recommendation. AAAI, 2017.

• Zhongxia et al. Co-Attentive Multi-Task Learning for Explainable Recommendation. IJCAI, 2019.

Explainability on RS

• Park et al. UniWalk: Explainable and Accurate Recommendation for Rating and Network Data. arXiv, 2017.

• Huang et al. Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks. SIGIR, 2018.

• Wang et al. Tem: Tree-enhanced embedding model for explainable recommendation. WWW, 2018.

• Lu et al. Why I like it: multi-task learning for recommendation and explanation. RecSys, 2018.

• Wang et al. Explainable Reasoning over Knowledge Graphs for Recommendation. AAAI, 2019.

• Cao et al. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. WWW, 2019.

• Zhongxia et al. Co-Attentive Multi-Task Learning for Explainable Recommendation. IJCAI, 2019.

• Min et al. Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach. IJCAI, 2019.

CTR Prediction for RS

• Guo et al. Deepfm: A factorization-machine based neural network for ctr prediction. IJCAI, 2017

• Zhou et al. Deep Interest Network for Click-Through Rate Prediction. KDD 2018.

• Zhou et al Deep Session Interest Network for Click-Through Rate Prediction. IJCAI, 2019.

• Zhou et al. Deep Interest Evolution Network for Click-Through Rate Prediction. AAAI, 2019

• Yang et al. Operation-aware Neural Networks for User Response Prediction. 2019.

• Liu et al. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction. 2019.

• Wentao et al. Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction. KDD, 2019.

• Qi et al. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction. KDD, 2019.

Knowledge Graph for RS

• Fuzheng et al. Collaborative Knowledge Base Embedding for Recommender Systems. KDD, 2016.

• Hongwei et al. DKN: Deep Knowledge-Aware Network for News Recommendation. WWW, 2018.

• Hongwei et al. Ripplenet-Propagating user preferences on the knowledge graph for recommender systems. CIKM, 2018.

• Hongwei et al. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. KDD, 2019.

• Hongwei et al. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. WWW, 2019.

• Xiang et al. Reinforced Negative Sampling over Knowledge Graph for Recommendation. WWW, 2020.

RSAlgorithms

Recently, we have launched an open source project RSAlgorithms, which provides an integrated training and testing framework. In this framework, we implement a set of classical traditional recommendation methods which make predictions only using rating data and social recommendation methods which utilize trust/social information in order to alleviate the sparsity of ratings data. Besides, we have collected some classical methods implemented by others for your convenience.

Acknowledgements

Specially summerize the papers about Recommender Systems for you, and if you have any questions, please contact me generously. Last but not least, the ability of myself is limited so I sincerely look forward to working with you to contribute it.

Thank @ShawnSu for collecting papers about POI Recommender Systems.

Thank @Wang Zhe for his advice about EE in RS.

Highly thank @Yujia Zhang for her summary on Hashing for RS.

Thank @Zixuan Yang for his collecting papers about CTR Prediction for RS.

Specially appreciate Professor @Jun Wu for his attentive guidance in my research career.

WeChat Official Account: ML-RSer

My ZhiHu: Honglei Zhang