/All-For-Recommendation

What You Want Is What We Want to Recommend

All-For-Recommendation

What You Want Is What We Want to Recommend

Conference会议

Classic Paper经典论文

  • Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12): 61-70.
    PDF

  • Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM conference on recommender systems. 2016: 191-198. PDF

  • Linden G, Smith B, York J. Amazon. com recommendations: Item-to-item collaborative filtering[J]. IEEE Internet computing, 2003, 7(1): 76-80. PDF

  • Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37. PDF

  • Rendle S. Factorization machines[C]//2010 IEEE International Conference on Data Mining. IEEE, 2010: 995-1000. PDF

Frontier Paper前沿论文

KDD2020

Research Track Papers

Sequential RS

Disentangled Self-Supervision in Sequential Recommenders
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
Geography-Aware Sequential Location Recommendation
Handling Information Loss of Graph Neural Networks for Session-based Recommendation
On Sampling Top-K Recommendation Evaluation
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

Conversational RS

Evaluating Conversational Recommender Systems via User Simulation
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
Interactive Path Reasoning on Graph for Conversational Recommendation

Cold-Start in RS

MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation

Collaborative Filtering

Dual Channel Hypergraph Collaborative Filtering
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation

Efficient RS

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems

Others

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks
BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
Joint Policy-Value Learning for Recommendation
On Sampled Metrics for Item Recommendation

Applied Data Science Track Papers

Google

Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies
Improving Recommendation Quality in Google Drive
Neural Input Search for Large Scale Recommendation Models

Alibaba

Controllable Multi-Interest Framework for Recommendation
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective
Privileged Features Distillation at Taobao Recommendations -Alibaba

Amazon

Temporal-Contextual Recommendation in Real-Time

Pinterest

PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest -Pinterest

Bytedance

Jointly Learning to Recommend and Advertise -Bytedance

DiDiChuxing

Gemini: A novel and universal heterogeneous graph information fusing framework for online recommendations

Twitter

SimClusters: Community-Based Representations for Heterogeneous Recommendations at Twitter

RecSys2020

Sequential RS

From the lab to production: A case study of session-based recommendations in the home-improvement domain.
ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation.
Exploring Longitudinal Effects of Session-based Recommendations.
Long-tail Session-based Recommendation.
Context-aware Graph Embedding for Session-based News Recommendation.
Investigating the Impact of Audio States & Transitions for Track Sequencing in Music Streaming Sessions.

Explainable RS

Explainable Recommendation for Repeat Consumption.
Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering.
Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners.

Unbiased and Fairness RS

Bias in Search and Recommender Systems
Debiasing Item-to-Item Recommendations With Small Annotated Datasets.
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems.
Unbiased Ad Click Prediction for Position-aware Advertising Systems.
Unbiased Learning for the Causal Effect of Recommendation.
Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning.
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation.
Counteracting Bias and Increasing Fairness in Search and Recommender Systems.
Counteracting Bias and Increasing Fairness in Search and Recommender Systems.
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation.
Fairness-aware Recommendation with librec-auto.
Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance.

SIGIR2020

Sequential RS

Modeling Personalized Item Frequency Information for Next-basket Recommendation.
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation.
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation.
Sequential Recommendation with Self-attentive Multi-adversarial Network.
A General Network Compression Framework for Sequential Recommender Systems.
Next-item Recommendation with Sequential Hypergraphs.
KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation.
Time Matters: Sequential Recommendation with Complex Temporal Information.

Graph-based RS

Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation.
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach.
Multi-behavior Recommendation with Graph Convolution Networks.
Hierarchical Fashion Graph Network for Personalised Outfit Recommendation.
Neighbor Interaction Aware Graph Convolution Networks for Recommendation.
Disentangled Representations for Graph-based Collaborative Filtering.

Cold-start RS

Content-aware Neural Hashing for Cold-start Recommendation.
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste.
Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation.
AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems.

Efficient RS

Lightening Graph Convolution Network for Recommendation.
A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data.
Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.
Online Collective Matrix Factorization Hashing for Large-Scale Cross-Media Retrieval.

Knowledge-aware RS

Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation.
Fairness-Aware Explainable Recommendation over Knowledge Graphs.
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View.
Make It a CHORUS: Context- and Knowledge-aware Item Modeling for Recommendation.
CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems.

Robustness RS

How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models.
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Identification.
How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models.
Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines.
DPLCF: Differentially Private Local Collaborative Filtering.
Data Poisoning Attacks against Differentially Private Recommender Systems.

Group RS

GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation.
GroupIM: A Mutual Information Maximizing Framework for Neural Group Recommendation.
Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation.
Global Context Enhanced Graph Nerual Networks for Session-based Recommendation.

Conversational RS

Deep Critiquing for VAE-based Recommender Systems.
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning.
Towards Question-based Recommender Systems.
Neural Interactive Collaborative Filtering.

RL for RS

Self-Supervised Reinforcement Learning for Recommender Systems.
MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations.
Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs.

Cross domain RS

Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation.
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network.

Explainable RS

Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations.
Try This Instead: Personalized and Interpretable Substitute Recommendation.

POI RS

HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation.

Others

Learning Personalized Risk Preferences for Recommendation.
Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters.
Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation.
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems.
The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation.
MVIN: Learning multiview items for recommendation.
How to Retrain a Recommender System?
Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems.
BiANE: Bipartite Attributed Network Embedding.
ASiNE: Adversarial Signed Network Embedding.
Learning Dynamic Node Representations with Graph Neural Networks.
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback.

WWW2020

Practical RS

Graph Enhanced Representation Learning for News Recommendation
Weakly Supervised Attention for Hashtag Recommendation using Graph Data
Personalized Employee Training Course Recommendation with Career Development Awareness
Understanding User Behavior For Document Recommendation
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
paper2repo: GitHub Repository Recommendation for Academic Papers

Sequential RS

Adaptive Hierarchical Translation-based Sequential Recommendation
Attentive Sequential Model of Latent Intent for Next Item Recommendation
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
Keywords Generation Improves E-Commerce Session-based Recommendation

Efficient RS

Learning to Hash with Graph Neural Networks for Recommender Systems
LightRec: a Memory and Search-Efficient Recommender System
A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems
Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation

Social RS

Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems
The Structure of Social Influence in Recommender Networks
Few-Shot Learning for New User Recommendation in Location-based Social Networks

Explainability for RS

Directional and Explainable Serendipity Recommendation
Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation

POI RS

Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices
A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data

General RS

Efficient Neural Interaction Function Search for Collaborative Filtering
Learning the Structure of Auto-Encoding Recommenders
Deep Global and Local Generative Model for Recommendation

Fairness in RS

Hierarchical Visual-aware Minimax Ranking Based on Co-purchase Data for Personalized Recommendation
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

RL for RS

Off-policy Learning in Two-stage Recommender Systems
Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation

Cross-domain RS

Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation

Knowledge Graph RS

Reinforced Negative Sampling over Knowledge Graph for Recommendation

Conversational RS

Latent Linear Critiquing for Conversational Recommender Systems

CTR for RS

Adversarial Multimodal Representation Learning for Click-Through Rate Prediction

ICML2020

Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Ordinal Non-negative Matrix Factorization for Recommendation
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

CIKM2020

CTR Prediction

Deep Time-Aware Item Evolution Network for Click-Through Rate Prediction
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Deep Multi-Interest Network for Click-through Rate Prediction
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-through Rate Prediction
Dimension Relation Modeling for Click-Through Rate Prediction
AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction
Ensembled CTR Prediction via Knowledge Distillation
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions

GNN based Recommendation

TGCN: Tag Graph Convolutional Network for Tag-Aware Recommendation
Star Graph Neural Networks for Session-based Recommendation
DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
Multiplex Graph Neural Networks for Multi-behavior Recommendation
Time-aware Graph Relational Attention Network for Stock Recommendation
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation
Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items

Knowledge Graph-enhanced Recommendation

News Recommendation with Topic-Enriched Knowledge Graphs
Multi-modal Knowledge Graphs for Recommender Systems
CAFE: Coarse-to-Fine Knowledge Graph Reasoning for E-Commerce Recommendation
MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings

Sequential Recommendation

Hybrid Sequential Recommender via Time-aware Attentive Memory Network
Improving End-to-End Sequential Recommendations with Intent-aware Diversification
Quaternion-based self-Attentive Long Short-term User Preference Encoding for Recommendation
Star Graph Neural Networks for Session-based Recommendation
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
DynamicRec: A Dynamic Convolutional Network for Next Item Recommendation

Text-based Recommendation

Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation
TPR: Text-aware Preference Ranking for Recommender Systems
News Recommendation with Topic-Enriched Knowledge Graphs
Transformer Models for Recommending Related Questions in Web Search
ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation

Job Recommendation

Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
Learning Effective Representations for Person-Job Fit by Feature Fusion

Location-based Recommendation

STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation
Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation
Magellan: A Personalized Travel Recommendation System Using Transaction Data

Social Recommendation

Partial Relationship Aware Influence Diffusion via Multi-channel Encoding Scheme for Social Recommendation
DREAM: A Dynamic Relation-Aware Model for social recommendation

Ranking/Re-ranking

Personalized Re-ranking with Item Relationships for E-commerce
Personalized Flight Itinerary Ranking at Fliggy
Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
E-commerce Recommendation with Weighted Expected Utility

Recommendation Diversity

ART (Attractive Recommendation Tailor): How the Diversity of Product Recommendations Affects Customer Purchase Preference in Fashion Industry?
P-Companion: A Principled Framework for Diversified Complementary Product Recommendation

Explainable Recommendation

Explainable Recommender Systems via Resolving Learning Representations
Generating Neural Template Explanations for Recommendation

User Profiling

Ranking User Attributes for Fast Candidate Selection in Recommendation Systems
Learning to Build User-tag Profile in Recommendation System
Masked-field Pre-training for User Intent Prediction

Online Advertising

Representative Negative Instance Generation for Online Ad Targeting
Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

Knowledge Distillation for Recommendation

DE-RRD: A Knowledge Distillation Framework for Recommender System
Ensembled CTR Prediction via Knowledge Distillation

AutoML for Recommendation

AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction

RL for Recommendation

Whole-Chain Recommendations
Cold-Start and Transfer Learning
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation
Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction

Bundle/Group Recommendation

Personalized Bundle Recommendation in Online Game

Debiasing

Feedback Loop and Bias Amplification in Recommender Systems
Exploring Missing Interactions: A Convolutional Generative Adversarial Network for Collaborative Filtering

Evaluation

Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
LensKit for Python: Next-Generation Software for Recommender Systems Experiments

New Scenarios

EdgeRec: Recommender System on Edge in Mobile Taobao
Leveraging Historical Interaction Data for Improving Conversational Recommender System

Others

Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
Selecting Influential Features by a Learnable Content-Aware Linear Threshold Model
Attacking Recommender Systems with Augmented User Profiles

WSDM2020

Addressing Marketing Bias in Product Recommendations. Mengting Wan, Jianmo Ni (University of California San Diego, United States); Rishabh Misra (Twitter, United States); Julian McAuley (University of California San Diego, United States).

ADMM SLIM: Sparse Recommendations for Many Users. Harald Steck, Maria Dimakopoulou, Nickolai Riabov (Netflix, United States); Tony Jebara (Spotify & Columbia University, United States).

Adversarial Learning to Compare: Self-Attentive Prospective Customer Recommendation in Location based Social Networks. Ruirui Li (University of California Los Angeles, United States); Xian Wu (University of Notre Dame, United States); Wei Wang (University of California Los Angeles, United States).

Consistency-Aware Recommendation for User-Generated Item List Continuation. Yun He, Yin Zhang (Texas A&M University, United States); Weiwen Liu (The Chinese University of Hong Kong); James Caverlee (Texas A&M University, United States).

DDTCDR: Deep Dual Transfer Cross Domain Recommendation. Pan Li, Alexander Tuzhilin (New York University, United States).

Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation. Yuan Zhang, Xiaoran Xu, Hanning Zhou, Yan Zhang (Peking University, China).

End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding. Feng Liu (Harbin Institute of Technology, China); Huifeng Guo (Huawei, China); Xutao Li (Harbin Institute of Technology, China); Ruiming Tang (Huawei, China); Yunming Ye (Harbin Institute of Technology, China); Xiuqiang He (Huawei, China).

Key Opinon Leaders in Recommendation Systems: Opinion Elicitation and Diffusion. Jianling Wang (Texas A&M University, United States); Kaize Ding (Arizona State University, United States); Ziwei Zhu, Yin Zhang, James Caverlee (Texas A&M University, United States).

LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation. Changfeng Sun, Han Liu, Meng Liu, Zhaochun Ren, Tian Gan, Liqiang Nie (Shandong University, China).

Learning a Joint Search and Recommendation Model from User-Item Interactions. Hamed Zamani, Bruce Croft (University of Massachusetts Amherst, United States).

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning. Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, Huan Liu (Arizona State University, United States).

Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation. Lixin Zou (Tsinghua University, China); Long Xia (JD.com, China); Pan Du (University of Montreal, Canada); Zhuo Zhang (The University of Melbourne, Australia); Ting Bai (Renmin University of China, China); Weidong Liu (Tsinghua University, China); Jian-Yun Nie (University of Montreal, Canada); Dawei Yin (JD.com, China).

RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback. Ilya Shenbin, Anton Alekseev, Elena Tutubalina (Steklov Institute of Mathematics at St. Petersburg, Russia); Valentin Malykh (Moscow Institute of Physics and Technology, Russia); Sergey Nikolenko (Steklov Institute of Mathematics at St. Petersburg, Russia).

Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation. Nengjun Zhu, Jian Cao (Shanghai Jiao Tong University, China); Yanchi Liu (Rutgers University, United States); Yang Yang (Nanjing University, China); Haochao Ying (Zhejiang University, China); Hui Xiong (Rutgers University, United States).

Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling. Jiarui Qin (Shanghai Jiao Tong University, China); Kan Ren (Microsoft, China); Yuchen Fang, Weinan Zhang, Yong Yu (Shanghai Jiao Tong University, China).

Time Interval Aware Self-Attention for Sequential Recommendation. Jiacheng Li (University of California San Diego, United States); Yujie Wang (Florida State University, United States); Julian McAuley (University of California San Diego, United States)

Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce. Jianling Wang (Texas A&M University, United States); Raphael Louca, Diane Hu, Caitlin Cellier (Etsy Inc., United States); James Caverlee (Texas A&M University, United States); Liangjie Hong (Etsy Inc., United States).

User Recommendation in Content Curation Platforms. Jianling Wang, Ziwei Zhu, James Caverlee (Texas A&M University, United States).

Hierarchical User Profiling for E-commerce Recommender Systems. Xing Zhao, Ziwei Zhu, Yin Zhang, James Caverlee (Texas A&M University, United States).

HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems. Lucas Vinh Tran, Yi Tay (Nanyang Technological University, Singapore); Shuai Zhang (The University of New South Wales, Australia); Gao Cong (Nanyang Technological University, Singapore); Xiaoli Li (Institute for Infocomm Research, Singapore).

Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations. Xing Zhao, Ziwei Zhu, Yin Zhang, James Caverlee (Texas A&M University, United States).

PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems. Azin Ghazimatin (Max Planck Institute for Informatics, Germany); Oana Balalau (Inria and École Polytechnique); Rishiraj Saha Roy, Gerhard Weikum (Max Plank Institute for Informatics, Germany).

Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. Yuta Saito (Tokyo Institute of Technology, Japan); Suguru Yaginuma, Yuta Nishino, Hayato Sakata (SMN Corporation, Japan); Kazuhide Nakata (Tokyo Institute of Technology, Japan).

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. Wenqiang Lei (National University of Singapore, Singapore); Xiangnan He (University of Science and Technology of China, China); Yisong Miao (National University of Singapore, Singapore); Qingyun Wu (University of virginia, United States); Richang Hong (Hefei University of Technology, China); Min-Yen Kan, Tat Seng Chua (National University of Singapore, Singapore).

AAAI 2021

RevMan: Revenue-Aware Multi-Task Online Insurance Recommendation
Detecting Beneficial Feature Interactions for Recommender Systems
FedRec++: Lossless Federated Recommendation with Explicit Feedback
Graph Heterogeneous Multi-Relational Recommendation
Hierarchical Reinforcement Learning for Integrated Recommendation
Who You Would Like to Share With? A Study of Share Recommendation in Social ECommerce
Self-Supervised Hypergraph Convolutional Networks for Session-Based Recommendation
Dual Sparse Attention Network for Session-Based Recommendation
U-BERT: Pre-Training User Representations for Improved Recommendation
Fairness-Aware News Recommendation with Decomposed Adversarial Learning
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
Cold-Start Sequential Recommendation via Meta Learner
A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models
A Hybrid Bandit Framework for Diversified Recommendation
PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
Noninvasive Self-Attention for Side Information Fusion in Sequential Recommendation
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
Out-of-Town Recommendation with Travel Intention Modeling
Learning to Recommend from Sparse Data via Generative User Feedback
Hierarchical Negative Binomial Factorization for Recommender Systems on Implicit Feedback
Disposable Linear Bandits for Online Recommendations
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
Dynamic Memory Based Attention Network for Sequential Recommendation
Asynchronous Stochastic Gradient Descent for Extreme-Scale Recommender Systems
On Estimating Recommendation Evaluation Metrics under Sampling
Knowledge-Aware Coupled Graph Neural Network for Social Recommendation
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-Based Recommendation
Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems
A General Offline Reinforcement Learning Framework for Interactive Recommendation
Intelligent Recommendations for Citizen Science
Degree Planning with PLAN-BERT: Multi-Semester Recommendation Using Future Courses of Interest
Personalized Adaptive Meta Learning for Cold-Start User Preference Prediction

IJCAI2020

Sequential RS

Adversarial Oracular Seq2seq Learning for Sequential RecommendationPengyu Zhao, Tianxiao Shui, Yuanxing Zhang, Kecheng Xiao, Kaigui Bian
Collaborative Self-Attention Network for Session-based RecommendationAnjing Luo, Pengpeng Zhao, Yanchi Liu, Fuzhen Zhuang, Deqing Wang, Jiajie Xu, Junhua Fang, Victor S. Sheng Memory Augmented Neural Model for Incremental Session-based RecommendationFei Mi, Boi Faltings

Cross-Domain RS

A Graphical and Attentional Framework for Dual-Target Cross-Domain RecommendationFeng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Xiaolin Zheng
Learning Personalized Itemset Mapping for Cross-Domain RecommendationYinan Zhang, Yong Liu, Peng Han, Chunyan Miao, Lizhen Cui, Baoli Li, Haihong Tang

POI RS

Contextualized Point-of-Interest RecommendationPeng Han, Zhongxiao Li, Yong Liu, Peilin Zhao, Jing Li, Hao Wang, Shuo Shang
Discovering Subsequence Patterns for Next POI RecommendationKangzhi Zhao, Yong Zhang, Hongzhi Yin, Jin Wang, Kai Zheng, Xiaofang Zhou, Chunxiao Xing
An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-insLu Zhang, Zhu Sun, Jie Zhang, Yu Lei, Chen Li, Ziqing Wu, Horst Kloeden, Felix Klanner

Explainable RS

Explainable Recommendation via Interpretable Feature Mapping and Evaluation of ExplainabilityDeng Pan, Xiangrui Li, Xin Li, Dongxiao Zhu
Synthesizing Aspect-Driven Recommendation Explanations from ReviewsTrung-Hoang Le, Hady W. Lauw
Towards Explainable Conversational RecommendationZhongxia Chen, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, Enhong Chen

News RS

User Modeling with Click Preference and Reading Satisfaction for News RecommendationChuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
HyperNews: Simultaneous News Recommendation and Active-Time Prediction via a Double-Task Deep Neural NetworkRui Liu, Huilin Peng, Yong Chen, Dell Zhang

Cold-Start In RS

Internal and Contextual Attention Network for Cold-start Multi-channel Matching in RecommendationRuobing Xie, Zhijie Qiu, Jun Rao, Yi Liu, Bo Zhang, Leyu Lin

General RS

Deep Feedback Network for RecommendationRuobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, Leyu Lin
Intent Preference Decoupling for User Representation on Online Recommender SystemZhaoyang Liu, Haokun Chen, Fei Sun, Xu Xie, Jinyang Gao, Bolin Ding, Yanyan Shen
Neural Tensor Model for Learning Multi-Aspect Factors in Recommender SystemsHuiyuan Chen, Jing Li

Datasets数据集