一直倍受关注的评分与评论问题:Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation
音乐推荐场景:Contextual and Sequential User Embeddings for Large-Scale Music Recommendation ;Carousel Personalization in Music Streaming Apps with Contextual Bandits
新闻推荐场景:KRED:Knowledge-Aware Document Representation for News Recommendations
信号预测场景:Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
视频推荐场景:Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de
隐私:Global and Local Differential Privacy for Collaborative Bandits
攻击:Revisiting Adversarially Learned Injection Attacks Against Recommender Systems
安全:Towards Safety and Sustainability_ Designing Local Recommendations for Post-pandemic World
4. 上下文话题也频繁出现。
上下文的Bandits问题:Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation ;Carousel Personalization in Music Streaming Apps with Contextual Bandits ;Contextual Meta-Bandit for Recommender Systems Selection
上下文的用户嵌入问题:Contextual and Sequential User Embeddings for Large-Scale Music Recommendation
结合注意力的上下文问题:TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations
游戏场景中的上下文:Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games
查询与上下文:Query as Context for Item-to-Item Recommendation
一种将业务指标匿名发布隐式反馈数据集的方法 | A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets
会话推荐系统潜在线性评论的一种排序优化方法 | A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems
我们是在评估对可重复评估和公平比较的严格的基准化建议吗 | Are We Evaluating Rigorously:Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison
级联的混合Bandits:在线学习的相关性和多样性排名 | Cascading Hybrid Bandits:Online Learning to Rank for Relevance and Diversity
增强推荐的内容协同解解耦表示学习 | Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation.
大规模音乐推荐的上下文和顺序用户嵌入 | Contextual and Sequential User Embeddings for Large-Scale Music Recommendation
上下文用户浏览bandits大规模在线移动推荐 | Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
使用小型带注释的数据集消除物品间推荐的偏差 | Debiasing Item-to-Item Recommendations With Small Annotated Datasets
解构过滤气泡:用户决策和推荐系统 | Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems.
双重鲁棒估计与点击后转换的排名指标 | Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions
通过对排名敏感的相关性平衡,确保群组推荐的公平性 | Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance
利用性能估计来增强推荐集合 | Exploiting Performance Estimates for Augmenting Recommendation Ensembles.
探索在线推荐系统的Bandits聚类 | Exploring Clustering of Bandits for Online Recommendation System
将项目相似度模型与自注意网络融合,进行顺序推荐 | FISSA:Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation
从实验室到生产:家庭改善领域基于会话的推荐案例研究 | From the lab to production: A case study of session-based recommendations in the home-improvement domain
全局和局部不同的隐私为协同Bandits | Global and Local Differential Privacy for Collaborative Bandits
针对分析师的目标驱动命令推荐 | Goal-driven Command Recommendations for Analysts
ImRec:学习互惠偏好使用图像 | ImRec:Learning Reciprocal Preferences Using Images
店内增强现实的产品比较和推荐 | In-Store Augmented Reality-Enabled Product Comparison and Recommendation
避免数据集的偏差在模拟:一个去偏的模拟器为强化学习基于推荐系统 | Keeping Dataset Biases out of the Simulation : A Debiased Simulator for Reinforcement Learning based Recommender Systems
KRED:用于新闻推荐的知识感知文档表示 | KRED:Knowledge-Aware Document Representation for News Recommendations
通过不需要交流的多智能体强化学习,学习在多模块推荐中协同 | Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
用属性使神经网络可解释:隐式信号预测的应用 | Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
MultiRec:拍卖系统中唯一物品推荐的一种多关系方法 | MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems
重新讨论神经协同过滤与矩阵分解 | Neural Collaborative Filtering vs. Matrix Factorization Revisited
线下背景多臂Bandits对移动健康的干预——以情绪调节为例 | Offline Contextual Multi-armed Bandits for Mobile Health Interventions:A Case Study on Emotion Regulation
离线推荐系统评价中的目标物品抽样问题 | On Target Item Sampling in Offline Recommender System Evaluation
渐进式分层抽取(PLE)_一种新的个性化推荐多任务学习(MTL)模型 | Progressive Layered Extraction (PLE)_ A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
PURS:个性化意外推荐系统,提高用户满意度 | PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
作为图探索的推荐 | Recommendations as Graph Explorations
推荐接下来观看的视频:在YOUTV.de进行线下和线上评估 | Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de
RecSeats:一个混合卷积神经网络选择模型,用于预订座位地点的座位推荐 | RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues
重新审视对推荐系统的对抗性注入攻击 | Revisiting Adversarially Learned Injection Attacks Against Recommender Systems
SSE-PT:通过个性化Transformer的顺序推荐 | SSE-PT:Sequential Recommendation Via Personalized Transformer
TAFA:双头注意力融合自动编码器,用于上下文感知推荐 | TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations
协同过滤推荐系统中用户发现迭代特性的理论建模 | Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System
朝向安全和可持续发展:为大流行后的世界制定地方推荐 | Towards Safety and Sustainability_ Designing Local Recommendations for Post-pandemic World
无偏的广告点击预测位置感知广告系统 | Unbiased Ad Click Prediction for Position-aware Advertising Systems
无偏学习推荐的因果效应 | Unbiased Learning for the Causal Effect of Recommendation
关于书、电影和音乐,BERT 知道些什么 | What does BERT know about books, movies and music:Probing BERT for Conversational Recommendation
“谁不喜欢恐龙”:发现并引出更丰富的推荐偏好 | "Who doesn't like dinosaurs": Finding and Eliciting Richer Preferences for Recommendation
Short Papers
自适应点对学习-排名基于内容的个性化推荐 | Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized Recommendation
ADER:自适应地提炼出回放范例,以实现基于会话的推荐的持续学习 | ADER:Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation
具有上下文bandits的音乐流应用中的轮播个性化 | Carousel Personalization in Music Streaming Apps with Contextual Bandits
推荐系统的因果推理 | Causal Inference for Recommender Systems
允许用户通过协同过滤和聚类来控制相关推荐 | ClusterExplorer_ Enable User Control over Related Recommendations via Collaborative Filtering and Clustering
将潜在因素模型与主题模型初始化,结合评分和评论数据进行Top-N推荐 | Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation
上下文的元bandits推荐系统选择 | Contextual Meta-Bandit for Recommender Systems Selection
从响应率偏差中消除用户满意度估计 | Deconfounding User Satisfaction Estimation from Response Rate Bias
深度贝叶斯bandits:探索在线个性化推荐 | Deep Bayesian Bandits:Exploring in Online Personalized Recommendations
重复使用的可解释的推荐 | Explainable Recommendation for Repeat Consumption
可解释的推荐,通过关注的多人物协同过滤 | Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
探索基于会话的推荐的纵向效应 | Exploring Longitudinal Effects of Session-based Recommendations
适合跑步:马拉松训练的个性化推荐 | Fit to Run:Personalised Recommendations for Marathon Training
免费午餐!在ROI约束内进行动态促销推荐的回顾性提升模型 | Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints
财务推荐的历史增强型协同过滤 | History-Augmented Collaborative Filtering for Financial Recommendations
在不同偏好强度下改进多任务的一类推荐 | Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
可解释的上下文团队意识项目推荐:在多人在线战场游戏中的应用 | Interpretable Contextual Team-aware Item Recommendation:Application in Multiplayer Online Battle Arena Games
混合注意机制与多时间嵌入顺序推荐 | MEANTIME:Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation
基于频率的双哈希方法减少推荐系统的模型尺寸 | Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
双曲几何模型在Top-N推荐任务上的性能 | Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
音乐推荐算法的个性化偏见 | Personality Bias of Music Recommendation Algorithms
为马拉松运动员提供可解释的比赛时间预测和训练计划建议 | Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners
通过实时推荐减少家庭能源浪费 | Reducing energy waste in households through real-time recommendations
基于组合联合学习的无偏内隐推荐和倾向估计 | Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning
用概念上的不协调作为提出推荐的基础 | Using conceptual incongruity as a basis for making recommendations
Industry Papers
人类对算法相似性的看法 | A Human Perspective on Algorithmic Similarity
平衡相关性和发现,在宜家应用中激发顾客 | Balancing Relevance and Discovery to Inspire Customers in the IKEA App
基于行为的亚马逊视频人气排名 | Behavior-based Popularity Ranking on Amazon Video
从日本一个著名的约会应用程序中学习,建立一个规模互惠的推荐系统 | Building a reciprocal recommendation system at scale from scratch_ Learnings from one of Japan's prominent dating applications
反事实学习的推荐系统 | Counterfactual learning for recommender system
开发推荐系统,为Chegg提供个性化的学习体验 | Developing Recommendation System to provide a Personalized Learning experience at Chegg
为Globoplay的视频推荐研究多模式特性 | Investigating Multimodal Features for Video Recommendations at Globoplay
关于工作领域的异构信息需求——一个统一的学生职业生涯平台 | On the Heterogeneous Information Needs in the Job Domain_ A Unified Platform for Student Career
查询作为Item到Item推荐的上下文 | Query as Context for Item-to-Item Recommendation
来自cold的嵌入:用基于内容的推断改进新和稀有产品的载体 | The Embeddings That Came in From the Cold:Improving Vectors for New and Rare Products with Content-Based Inference