Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations
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[1] M. Jalili,et al. A deep learning based trust- and tag-aware recommender system , 2021, Neurocomputing.
[2] S. Nahavandi,et al. An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis , 2021, Applied Soft Computing.
[3] Xiaofeng Gao,et al. Trust Prediction for Online Social Networks with Integrated Time-Aware Similarity , 2021, ACM Trans. Knowl. Discov. Data.
[4] Alexander Tuzhilin,et al. Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations , 2021, IEEE Transactions on Knowledge and Data Engineering.
[5] Jiadong Ren,et al. Deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations , 2021, Knowl. Based Syst..
[6] Guanfeng Liu,et al. Cross-Domain Recommendation: Challenges, Progress, and Prospects , 2021, IJCAI.
[7] Umer Rashid,et al. On deep neural network for trust aware cross domain recommendations in E-commerce , 2021, Expert Syst. Appl..
[8] Peipei Li,et al. Collaborative filtering with a deep adversarial and attention network for cross-domain recommendation , 2021, Inf. Sci..
[9] Adeel Ahmed,et al. Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks , 2020, IEEE Access.
[10] Himanshu Aggarwal,et al. AutoTrustRec: Recommender System with Social Trust and Deep Learning using AutoEncoder , 2020, Multimedia Tools and Applications.
[11] Pan Li,et al. DDTCDR: Deep Dual Transfer Cross Domain Recommendation , 2019, WSDM.
[12] Atta ur Rehman Khan,et al. SocialRec: A Context-Aware Recommendation Framework With Explicit Sentiment Analysis , 2019, IEEE Access.
[13] Jungwoo Lee,et al. Scalable deep learning-based recommendation systems , 2019, ICT Express.
[14] Rui Liu,et al. A General Cross-Domain Recommendation Framework via Bayesian Neural Network , 2018, 2018 IEEE International Conference on Data Mining (ICDM).
[15] Sambhav Yadav,et al. Trust aware recommender system using swarm intelligence , 2018, J. Comput. Sci..
[16] Parham Moradi,et al. AN EFFICIENT RECOMMENDER SYSTEM BY INTEGRATING NON-NEGATIVE MATRIX FACTORIZATION WITH TRUST AND DISTRUST RELATIONSHIPS , 2018, 2018 IEEE Data Science Workshop (DSW).
[17] Yu Zhang,et al. CoNet: Collaborative Cross Networks for Cross-Domain Recommendation , 2018, UMCit@KDD.
[18] Vinod Chandran,et al. Facial Expression Analysis under Partial Occlusion , 2018, ACM Comput. Surv..
[19] Gabriel Tamura,et al. Characterizing context-aware recommender systems: A systematic literature review , 2018, Knowl. Based Syst..
[20] Tiejian Luo,et al. A Recommendation Model Based on Deep Neural Network , 2018, IEEE Access.
[21] Francisco Charte,et al. A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines , 2018, Inf. Fusion.
[22] Xu Chen,et al. Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources , 2017, CIKM.
[23] Tat-Seng Chua,et al. Neural Factorization Machines for Sparse Predictive Analytics , 2017, SIGIR.
[24] Seung-won Hwang,et al. Efficient Keyword-Aware Representative Travel Route Recommendation , 2017, IEEE Transactions on Knowledge and Data Engineering.
[25] S. C. Hui,et al. Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking , 2017, WWW.
[26] Roliana Ibrahim,et al. Cross Domain Recommender Systems , 2017, ACM Comput. Surv..
[27] Zhaohui Wu,et al. On Deep Learning for Trust-Aware Recommendations in Social Networks , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[28] Tat-Seng Chua,et al. Neural Collaborative Filtering , 2017, WWW.
[29] Deborah Estrin,et al. Collaborative Metric Learning , 2017, WWW.
[30] Yunming Ye,et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.
[31] Fazhi He,et al. A correlative denoising autoencoder to model social influence for top-N recommender system , 2017, Frontiers of Computer Science.
[32] Kai Chen,et al. Collaborative filtering and deep learning based recommendation system for cold start items , 2017, Expert Syst. Appl..
[33] Yun Fu,et al. Examples-Rules Guided Deep Neural Network for Makeup Recommendation , 2017, AAAI.
[34] Heng-Tze Cheng,et al. Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.
[35] Alexander Maedche,et al. Using Gamification to Tackle the Cold-Start Problem in Recommender Systems , 2016, CSCW Companion.
[36] Martin Ester,et al. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems , 2016, WSDM.
[37] Parham Moradi,et al. An effective trust-based recommendation method using a novel graph clustering algorithm , 2015 .
[38] Talel Abdessalem,et al. POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences , 2015, RecSys.
[39] Scott Sanner,et al. AutoRec: Autoencoders Meet Collaborative Filtering , 2015, WWW.
[40] Daqing Zhang,et al. Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[41] Guandong Xu,et al. Social network-based service recommendation with trust enhancement , 2014, Expert Syst. Appl..
[42] Paolo Cremonesi,et al. Cross-domain recommendations without overlapping data: myth or reality? , 2014, RecSys '14.
[43] Dit-Yan Yeung,et al. Collaborative Deep Learning for Recommender Systems , 2014, KDD.
[44] Nan Zhang,et al. Adaptive ensemble with trust networks and collaborative recommendations , 2014, Knowledge and Information Systems.
[45] E. Oja,et al. Improving cluster analysis by co-initializations , 2014, Pattern Recognit. Lett..
[46] Mehrnoush Shamsfard,et al. Hybrid PoS-tagging: A cooperation of evolutionary and statistical approaches , 2014 .
[47] Albert Y. Zomaya,et al. OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for Mobile Social Networks , 2014, IEEE Transactions on Services Computing.
[48] N. Lalithamani,et al. SENTENCE-LEVEL SENTIMENT POLARITY CALCULATION FOR CUSTOMER REVIEWS BY CONSIDERING COMPLEX SENTENTIAL STRUCTURES , 2014 .
[49] Larry P. Heck,et al. Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.
[50] Masnizah Mohd,et al. Enhanced Arabic Information Retrieval: Light Stemming and Stop Words , 2013, M-CAIT.
[51] Yung-Ming Li,et al. A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship , 2013, Decis. Support Syst..
[52] Vivek Narayanan,et al. Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model , 2013, IDEAL.
[53] Huan Liu,et al. eTrust: understanding trust evolution in an online world , 2012, KDD.
[54] André Stuhlsatz,et al. Feature Extraction With Deep Neural Networks by a Generalized Discriminant Analysis , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[55] Perumal Pitchandi,et al. Improving the Performance of Multivariate Bernoulli Model based Documents Clustering Algorithms using Transformation Techniques , 2011 .
[56] Korris Fu-Lai Chung,et al. A probabilistic rating inference framework for mining user preferences from reviews , 2011, World Wide Web.
[57] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[58] Reza Boostani,et al. An Efficient Initialization Method for Nonnegative Matrix Factorization , 2011 .
[59] Qiang Yang,et al. Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction , 2009, IJCAI.
[60] Yehuda Koren,et al. Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.
[61] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[62] Jun Wang,et al. Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.
[63] John Riedl,et al. Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.
[64] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[65] R. L. Thorndike. Who belongs in the family? , 1953 .
[66] Jaouad Boumhidi,et al. An intelligent recommender system using social trust path for recommendations in web-based social networks , 2019, Procedia Computer Science.
[67] Parham Moradi,et al. TCARS: Time- and Community-Aware Recommendation System , 2018, Future Gener. Comput. Syst..
[68] Yuexuan Wang,et al. Leveraging Transitive Trust Relations to Improve Cross-Domain Recommendation , 2018, IEEE Access.
[69] Roberto Turrin,et al. Cross-Domain Recommender Systems , 2015, Recommender Systems Handbook.
[70] Moumita Roy,et al. Empirical Study of Different Classifiers for Sentiment Analysis , 2014 .
[71] Michal Munk,et al. Data Pre-Processing Evaluation for Text Mining: Transaction/Sequence Model , 2013, ICCS.
[72] Balázs Hidasi,et al. Initializing Matrix Factorization Methods on Implicit Feedback Databases , 2013, J. Univers. Comput. Sci..
[73] Qiang Yang,et al. Contextual Collaborative Filtering via Hierarchical Matrix Factorization , 2012, SDM.
[74] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2009 .
[75] Amélie Marian,et al. Beyond the Stars: Improving Rating Predictions using Review Text Content , 2009, WebDB.
[76] L. Zucker. Production of trust: Institutional sources of economic structure, 1840–1920. , 1986 .