Machine Learning -- MA-TREX: Multi-agent Trajectory-ranked Reward Extrapolation via Inverse Reinforcement Learning -- An Incremental Learning Network Model based on Random Sample Distribution Fitting -- Parameter Optimization and Weights Assessment for Evidential Artificial Immune Recognition System -- Improving Policy Generalization for Teacher-Student Reinforcement Learning -- Recommendation Algorithms and Systems -- Towards effective top-k location recommendation for business facility placement -- Pairwise-based Hierarchical Gating Networks for Sequential Recommendation -- Time-aware Attentive Neural Network for News Recommendation with Long- and Short-Term User Representation -- A Time Interval Aware Approach for Session-based Social Recommendation -- AutoIDL: Automated Imbalanced Data Learning via Collaborative Filtering -- Fusion of Domain Knowledge and Text Features for Query Expansion in Citation Recommendation -- Robust Sequence Embedding for Recommendation -- Deep Generative Recommendation with Maximizing Reciprocal Rank -- Spatio-Temporal Attentive Network for Session-based Recommendation -- Social Knowledge Analysis and Management -- Category-level Adversarial Network for Cross-Domain Sentiment Classification -- Seeds Selection for Influence Maximization Based on Device-to-Device Social Knowledge by Reinforcement Learning -- CIFEF: Combining implicit and explicit features for friendship inference in location-based social networks -- A Knowledge Enhanced Ensemble Learning Model for Mental Disorder Detection on Social Media -- Constrained Viral Marketing in Social Networks -- A Multi-source Self-adaptive Transfer Learning Model for Mining Social Links -- Text Mining and Document Analysis -- Multi-hop Syntactic Graph Convolutional Networks for Aspect-based Sentiment Classification -- A Matching-Integration-Verification Model for Multiple-Choice Reading Comprehension -- How to interact and change? Abstractive Dialogue Summarization with Dialogue Act Weight and Topic Change Info -- Chinese Text Classification via Bidirectional Lattice LSTM -- MG-BERT: A Multi-Glosses BERT Model for Word Sense Disambiguation -- Top Personalized Reviews Set Selection Based on Subject Aspect Modeling -- SCX-SD: Semi-Supervised Method for Contextual Sarcasm Detection -- End-to-End Multi-task Learning for Allusion Detection in Ancient Chinese Poems -- Defense of Word-level Adversarial Attacks via Random Substitution Encoding -- Document-Improved Hierarchical Modular Attention for Event Detection -- Fine-tuned Transformer Model for Sentiment Analysis -- An Algorithm for Emotion Evaluation and Analysis Based on CBOW -- Predicting Crowdsourcing Worker Performance with Knowledge Tracing -- Deep Learning -- Watermarking neural network with compensation mechanism -- Information Diffusion Prediction with Personalized Graph Neural Networks -- Relationship-Aware Hard Negative Generation in Deep Metric Learning -- Striking a Balance in Unsupervised Fine-grained Domain Adaptation Using Adversarial Learning -- Improved Performance of GANs via Integrating Gradient Penalty with Spectral Normalization -- Evidential Deep Neural Networks for Uncertain Data Classification -- GDCRN: Global Diffusion Convolutional Residual Network for Traffic Flow Prediction -- Depthwise Separable Convolutional Neural Network for Confidential Information Analysis -- The Short-Term Exit Traffic Prediction of A Toll Station Based on LSTM -- Long and Short Term Risk Control for Online Portfolio Selection.

This two-volume set of LNAI 12274 and LNAI 12275 constitutes the refereed proceedings of the 13th International Conference on Knowledge Science, Engineering and Management, KSEM 2020, held in Hangzhou, China, in August 2020.* The 58 revised full papers and 27 short papers were carefully reviewed and selected from 291 submissions. The papers of the first volume are organized in the following topical sections: knowledge graph; knowledge representation; knowledge management for education; knowledge-based systems; and data processing and mining. The papers of the second volume are organized in the following topical sections: machine learning; recommendation algorithms and systems; social knowledge analysis and management; text mining and document analysis; and deep learning. *The conference was held virtually due to the COVID-19 pandemic.