recsys 2020 论文列表
RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22-26, 2020.
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Taking advantage of images and texts in recommender systems: semantics and explainability.
Online Recommender system for Accessible Tourism Destinations.
Exploratory Methods for Evaluating Recommender Systems.
Evolutionary Approach in Recommendation Systems for Complex Structured Objects.
Efficiency-Effectiveness Trade-offs in Recommendation Systems.
Developing Work in Confidence, Similarity Structure, and Modeling User Event Time.
Conversational Agents for Recommender Systems.
Characterizing and Mitigating the Impact of Data Imbalance for Stakeholders in Recommender Systems.
Tutorial: Feature Engineering for Recommender Systems.
Tutorial on Conversational Recommendation Systems.
Introduction to Bandits in Recommender Systems.
Counteracting Bias and Increasing Fairness in Search and Recommender Systems.
Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendation.
Adversarial Learning for Recommendation: Applications for Security and Generative Tasks - Concept to Code.
Tuning Word2vec for Large Scale Recommendation Systems.
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation.
Smart Targeting: A Relevance-driven and Configurable Targeting Framework for Advertising System.
Recommending in changing times.
Towards Multi-Language Recipe Personalisation and Recommendation.
Learning Representations of Hierarchical Slates in Collaborative Filtering.
Investigating the Impact of Audio States & Transitions for Track Sequencing in Music Streaming Sessions.
Investigating Listeners' Responses to Divergent Recommendations.
Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions.
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models.
DRecPy: A Python Framework for Developing Deep Learning-Based Recommenders.
"Don't Judge a Book by its Cover": Exploring Book Traits Children Favor.
Do Channels Matter? Illuminating Interpersonal Influence on Music Recommendations.
Context-aware Graph Embedding for Session-based News Recommendation.
Closed-Form Models for Collaborative Filtering with Side-Information.
A Joint Dynamic Ranking System with DNN and Vector-based Clustering Bandit.
A College Major Recommendation System.
Workshop on Online Misinformation- and Harm-Aware Recommender Systems.
Workshop on Context-Aware Recommender Systems.
Second Workshop on Recommender Systems in Fashion - fashionXrecsys2020.
Second Workshop on the Impact of Recommender Systems at ACM RecSys '20.
REVEAL 2020: Bandit and Reinforcement Learning from User Interactions.
RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter's Home Timeline.
PodRecs: Workshop on Podcast Recommendations.
ORSUM - Workshop on Online Recommender Systems and User Modeling.
Interfaces and Human Decision Making for Recommender Systems.
Fifth International Workshop on Health Recommender Systems (HealthRecSys 2020).
ComplexRec 2020: Workshop on Recommendation in Complex Environments.
3rd FAccTRec Workshop: Responsible Recommendation.
VMI-PSL: Visual Model Inspector for Probabilistic Soft Logic.
Recommender-Systems.com: A Central Platform for the Recommender-System Community.
PicTouRe - A Picture-Based Tourism Recommender.
Fairness-aware Recommendation with librec-auto.
Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG.
BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems.
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization.
AutoRec: An Automated Recommender System.
A Federated Recommender System for Online Services.
The Embeddings That Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based Inference.
Query as Context for Item-to-Item Recommendation.
On the Heterogeneous Information Needs in the Job Domain: A Unified Platform for Student Career.
Investigating Multimodal Features for Video Recommendations at Globoplay.
Developing Recommendation System to provide a Personalized Learning experience at Chegg.
Counterfactual learning for recommender system.
Building a reciprocal recommendation system at scale from scratch: Learnings from one of Japan's prominent dating applications.
Behavior-based Popularity Ranking on Amazon Video.
Balancing Relevance and Discovery to Inspire Customers in the IKEA App.
A Human Perspective on Algorithmic Similarity.
Using conceptual incongruity as a basis for making recommendations.
Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning.
Reducing energy waste in households through real-time recommendations.
Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners.
Personality Bias of Music Recommendation Algorithms.
Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks.
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems.
MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation.
Long-tail Session-based Recommendation.
Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games.
Improving One-class Recommendation with Multi-tasking on Various Preference Intensities.
History-Augmented Collaborative Filtering for Financial Recommendations.
Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints.
Fit to Run: Personalised Recommendations for Marathon Training.
Exploring Longitudinal Effects of Session-based Recommendations.
Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering.
Explainable Recommendation for Repeat Consumption.
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations.
Deconfounding User Satisfaction Estimation from Response Rate Bias.
Contextual Meta-Bandit for Recommender Systems Selection.
Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation.
ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering.
Causal Inference for Recommender Systems.
Carousel Personalization in Music Streaming Apps with Contextual Bandits.
Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized Recommendation.
ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation.
"Who doesn't like dinosaurs?" Finding and Eliciting Richer Preferences for Recommendation.
What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation.
Unbiased Learning for the Causal Effect of Recommendation.
Unbiased Ad Click Prediction for Position-aware Advertising Systems.
Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World.
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System.
TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations.
SSE-PT: Sequential Recommendation Via Personalized Transformer.
Revisiting Adversarially Learned Injection Attacks Against Recommender Systems.
RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues.
Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de.
Recommendations as Graph Explorations.
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction.
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations.
On Target Item Sampling in Offline Recommender System Evaluation.
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation.
Neural Collaborative Filtering vs. Matrix Factorization Revisited.
MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems.
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction.
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication.
KRED: Knowledge-Aware Document Representation for News Recommendations.
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems.
In-Store Augmented Reality-Enabled Product Comparison and Recommendation.
ImRec: Learning Reciprocal Preferences Using Images.
Goal-driven Command Recommendations for Analysts.
Global and Local Differential Privacy for Collaborative Bandits.
From the lab to production: A case study of session-based recommendations in the home-improvement domain.
FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation.
Exploring Clustering of Bandits for Online Recommendation System.
Exploiting Performance Estimates for Augmenting Recommendation Ensembles.
Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance.
Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions.
Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems.
Debiasing Item-to-Item Recommendations With Small Annotated Datasets.
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation.
Contextual and Sequential User Embeddings for Large-Scale Music Recommendation.
Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation.
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity.
Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison.
A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems.
A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets.
"You Really Get Me": Conversational AI Agents That Can Truly Understand and Help Users.
Bias in Search and Recommender Systems.
4 Reasons Why Social Media Make Us Vulnerable to Manipulation.
