SIGIR-2020.md

CTR Prediction

1. 【华为】AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction
2. 【华为】Multi-Branch Convolutional Network for Context-Aware Recommendation
3. 【腾讯】TFNet: Multi-Semantic Feature Interaction for CTR Prediction
4. 【阿里】Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction
5. 【阿里】ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
6. 【第四范式】Network On Network for Tabular Data Classification in Real-world Applications
7. User Behavior Retrieval for Click-Through Rate Prediction

GNN-based Recommendation

1. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
2. Disentangled Graph Collaborative Filtering
3. 【阿里、蚂蚁金服】ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
4. GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
5. 【华为】Neighbor Interaction Aware Graph Convolution Networks for Recommendation
6. Next-item Recommendation with Sequential Hypergraphs
7. Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation
8. Global Context Enhanced Graph Nerual Networks for Session-based Recommendation
9. Bundle Recommendation with Graph Convolutional Networks
10. A Heterogeneous Graph Neural Model for Cold-Start Recommendation
11. TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
12. Multi-Behavior Recommendation with Graph Convolution Networks
13. GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Identification

Knowledge-Graph Enhanced Recommendation

1. Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
2. Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation
3. Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
4. Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs
5. Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
6. A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention
7. Fairness-Aware Explainable Recommendation over Knowledge Graphs
8. Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
9. Make It a CHORUS: Context- and Knowledge-aware Item Modeling for Recommendation
10. ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
11. CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems

User Interest Modeling

1. MRIF: Multi-Resolution Interest Fusion for Recommendation
2. 【阿里】Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction

Multi-Behavior Modeling

1. Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
2. GMCM: Graph-based Micro-behavior Conversion Model for Post-click Conversion Rate Estimation
3. Multi-Behavior Recommendation with Graph Convolution Networks

Multi-Task Learning

1. 【阿里】Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
2. Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
3. 【华为】JIT$^2$R:A Joint Framework for Item Tagging and Tag-based Recommendation

Transfer Learning

1. Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
2. Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
3. A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users
4. Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
5. Web-to-Voice Transfer for Product Recommendation on Voice
6. CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
7. ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

RL for Recommendation

1. Reinforcement Learning based Recommendation with Graph Convolutional Q-network
2. Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
3. Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation
4. Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic
5. MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations
6. Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs
7. Self-Supervised Reinforcement Learning for Recommender Systems
8. Reinforcement Learning to Rank with Pairwise Policy Gradient

Sequential Recommendation

1. Session-based Recommendation with Hierarchical Leaping Networks
2. Sequential-based Adversarial Optimisation for Personalised Top-N Item Recommendation
3. Rethinking Item Importance in Session-based Recommendation
4. An Intent-guided Collaborative Machine for Session-based Recommendation
5. Sentiment-guided Sequential Recommendation
6. Sequential Recommendation with Self-attentive Multi-adversarial Network
7. Global Context Enhanced Graph Nerual Networks for Session-based Recommendation
8. Time Matters: Sequential Recommendation with Complex Temporal Information
9. Dual Sequential Network for Temporal Sets Prediction
10. Modeling Personalized Item Frequency Information for Next-basket Recommendation
11. A General Network Compression Framework for Sequential Recommender Systems
12. Next-item Recommendation with Sequential Hypergraphs
13. KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation

Multimodal Recommendation

1. Hierarchical Fashion Graph Network for Personalised Outfit Recommendation
2. Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation
3. Fashion Compatibility Modeling through a Multi-modal Try-on-guided Scheme
4. Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Text-based Recommendation

1. Neural Unified Review Recommendation with Cross Attention
2. How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements
3. MVL: Multi-View Learning for News Recommendation

Cold-Start Recommendation

1. Joint Training Capsule Network for Cold Start Recommendation
2. A Heterogeneous Graph Neural Model for Cold-Start Recommendation
3. Content-aware Neural Hashing for Cold-start Recommendation
4. Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation
5. DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain
6. AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems

Debiasing

1. Using Exploration to Alleviate Closed-Loop Effects in Recommender Systems
2. Influence Function for Unbiased Recommendation
3. A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data
4. Modeling Personalized Item Frequency Information for Next-basket Recommendation
5. Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems
6. A Deep Recurrent Survival Model for Unbiased Ranking
7. Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback

Explainable Recommendation

1. User-Inspired Posterior Network for Recommendation Reason Generation
2. Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations
3. Try This Instead: Personalized and Interpretable Substitute Recommendation
4. Fairness-Aware Explainable Recommendation over Knowledge Graphs

Points-Of-Interest Recommendation

1. Relevance Models for Multi-Contextual Appropriateness in Points-Of-Interest Recommendation
2. HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation
3. Spatial Object Recommendation with Hints: When Spatial Granularity Matters

Group/Bundle Recommendation

1. GroupIM: A Mutual Information Maximizing Framework for Neural Group Recommendation
2. GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation
3. Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation
4. Bundle Recommendation with Graph Convolutional Networks

Embedding

1. Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation
2. Automated Embedding Size Search in Deep Recommender Systems

Representation Learning for Recommendation

1. MVIN: Learning multiview items for recommendation
2. HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation
3. Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates
4. Disentangled Representations for Graph-based Collaborative Filtering
5. Neural Interactive Collaborative Filtering

Re-Ranking

1. Contextual Re-Ranking with Behavior Aware Transformers

AutoML for Recommendation

1. Automated Embedding Size Search in Deep Recommender Systems

Federated Learning

1. Meta Matrix Factorization for Federated Rating Predictions

Recommendation Diversity

1. Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs

Multi-Objective Optimization

1. Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation

Training

1. 【快手】How to Retrain a Recommender System? A Sequential Meta-Learning Method
2. Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
3. Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation

Content Tagging

1. 【华为】Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
2. 【华为】JIT$^2$R:A Joint Framework for Item Tagging and Tag-based Recommendation

Security and Privacy

1. Data Poisoning Attacks against Differentially Private Recommender Systems
2. Adversarial Attack and Detection on Reinforcement Learning based Recommendation System
3. DPLCF: Differentially Private Local Collaborative Filtering
4. Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines

Benchmarks and Evaluation

1. A Re-visit of Popularity Baseline in Recommender Systems
2. How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models
3. Visual Intents vs. Clicks, Likes, and Purchases in E-commerce
4. Deep Critiquing for VAE-based Recommender Systems
5. Understanding Echo Chambers in E-commerce Recommender Systems
6. Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation
7. The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation

Others

1. Learning Personalized Risk Preferences for Recommendation
2. Towards Question-based Recommender Systems
3. How Airbnb Tells You Will Enjoy Sunset Sailing in Barcelona? Recommendation in a Two-Sided Travel Marketplace