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WWW 2022接收論文已經發布。這次共收到了1822篇論文，接收323篇，錄用率為17.7%。完整清單見下面的鏈接

https://www2022.thewebconf.org/accepted-papers/

本文對廣告和推薦相關的文章進行了整理和分類，廣告的分在了一個大類，推薦相關的分為了冷啟動，會話推薦，序列推薦，CTR預測相關，糾偏相關，可解釋性，采樣方法等領域，根據使用的技術方法分為了圖學習，因果關系，強化學習，多任務學習等希望對大家有所幫助。

廣告

Equilibria in Auctions with Ad Types【廣告類型的拍賣均衡】

On Designing a Two-stage Auction for Online Advertising【論網絡廣告的兩階段拍賣設計】

Auction design in an autobidding setting: Randomization improves efficiency beyond VCG【自動出價設置中的拍賣設計：隨機化提高了 VCG 之外的效率】

Auctions Between Regret Minimizing Agents【遺憾最小化代理之間的拍賣】

Beyond Customer Lifetime Valuation: Measuring the Value of Acquisition and Retention for Subscription Services【超越客戶終身估值：衡量訂閱服務的獲取和保留價值】

Calibrated Click-Through Auctions【校準的點擊式拍賣】

Cross DQN: Cross Deep Q Network for Ads Allocation in Feed【Cross DQN：用于 Feed 中廣告分配的 Cross Deep Q Network】

Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions【首次價格拍賣中基于均值的學習算法的納什收斂】

Price Manipulability in First-Price Auctions【首價拍賣中的價格操縱性】

The Parity Ray Regularizer for Pacing in Auction Markets【用于拍賣市場節奏的 Parity Ray 正則化器】

推薦

冷啟動

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework【使用變分嵌入學習框架緩解 CTR 預測中的冷啟動問題】

PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation【PMNTA：一種用于用戶冷啟動推薦的預訓練網絡調制和任務適應方法】

KoMen: Domain Knowledge Guided Interaction Recommendation for Emerging Scenarios【KoMen：新興場景的領域知識引導交互推薦】

點擊CTR

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework【使用變分嵌入學習框架緩解 CTR 預測中的冷啟動問題】

Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation【觸發誘導推薦中點擊率預測的深度興趣突出網絡】

ParClick: A Scalable Algorithm for EM-based Click Models【ParClick：基于 EM 的 Click 模型的可擴展算法】

CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction【CBR：用于消除用戶建模和點擊預測的上下文偏差感知建議】

Rating Distribution Calibration for Selection Bias Mitigation in Recommendations【推薦中減少選擇偏差的評級分布校準】

MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration【MBCT：用于個體不確定性校準的基于樹的特征感知分箱】

session會話推薦

Generative Session-based Recommendation【基于生成會話的推薦】

GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction【GSL4Rec：具有集體圖結構學習和下一次交互預測的基于會話的推薦】

sequential序列推薦

Efficient Online Learning to Rank for Sequential Music Recommendation【高效的在線學習對順序音樂推薦進行排名】

Filter-enhanced MLP is All You Need for Sequential Recommendation【過濾器增強的 MLP 是進行順序推薦所需的全部】

Intent Contrastive Learning for Sequential Recommendation【序列推薦的意圖對比學習】

Learn from Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data【從過去學習，為未來發展：基于搜索的時間感知推薦與順序行為數據】

Sequential Recommendation via Stochastic Self-Attention【通過隨機自注意力的順序推薦】

Sequential Recommendation with Decomposed Item Feature Routing【具有分解項目特征路由的順序推薦】

Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation【面向順序推薦的深度混合網絡架構的自動發現】

Unbiased Sequential Recommendation with Latent Confounders【具有潛在混雜因素的無偏順序推薦】

Disentangling Long and Short-Term Interests for Recommendation【解耦推薦的長期和短期利益】

跨域

Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation【用于基于評論的非重疊跨域推薦的具有屬性對齊的協同過濾】

Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation【隱私保護跨域推薦的差分私有知識遷移】

Learning Robust Recommenders through Cross-Model Agreement【通過跨模型協議學習強大的推薦器】

糾偏

Cross Pairwise Ranking for Unbiased Item Recommendation【無偏項目推薦的交叉成對排名】

CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction【CBR：用于消除用戶建模和點擊預測的上下文偏差感知建議】

Unbiased Sequential Recommendation with Latent Confounders【具有潛在混雜因素的無偏順序推薦】

Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction【通過標簽校正的延遲反饋建模的漸近無偏估計】

UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation【UKD：通過不確定性正則化知識蒸餾的去偏轉換率估計】

個性化

Regulatory Instruments for Fair Personalized Pricing【公平個性化定價的監管工具】

Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors【使用概念激活向量發現推薦系統中軟屬性的個性化語義】

Improving Personalized Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks【通過調整輔助任務的梯度幅度來改進個性化推薦】

An Empirical Investigation of Personalization Factors on TikTok【TikTok個性化因素的實證研究】

VisGNN: Personalized Visualization Recommendation via Graph Neural Networks【VisGNN：基于圖神經網絡的個性化可視化推薦】

因果關系

A Model-Agnostic Causal Learning Framework for Recommendation using Search Data【使用搜索數據進行推薦的與模型無關的因果學習框架】

Causal Preference Learning for Out-of-Distribution Recommendation【分布外推薦的因果偏好學習】

Unbiased Sequential Recommendation with Latent Confounders【具有潛在混雜因素的無偏順序推薦】

可解釋

VisGNN: Personalized Visualization Recommendation via Graph Neural Networks【VisGNN：基于圖神經網絡的個性化可視化推薦】

Path Language Modeling over Knowledge Graphs for Explainable Recommendation【可解釋推薦的知識圖路徑語言建模】

Accurate and Explainable Recommendation via Review Rationalization【通過審查合理化提供準確且可解釋的建議】

AmpSum: Adaptive Multiple-Product Summarization towards Improving Recommendation Explainability【AmpSum：提高推薦可解釋性的自適應多產品總結】

Comparative Explanations of Recommendations【推薦系統的比較解釋】

Neuro-Symbolic Interpretable Collaborative Filtering for Attribute-based Recommendation【基于屬性推薦的神經符號可解釋協同過濾】

公平性

FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback【FairGAN：基于 GAN 的公平感知學習，用于具有隱式反饋的推薦】

Regulatory Instruments for Fair Personalized Pricing【公平個性化定價的監管工具】

Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation【隱私保護跨域推薦的差分私有知識遷移

多任務

A Multi-task Learning Framework for Product Ranking with BERT【使用 BERT 進行產品排名的多任務學習框架】

A Contrastive Sharing Model for Multi-Task Recommendation【多任務推薦的對比共享模型】

對比學習

Intent Contrastive Learning for Sequential Recommendation【序列推薦的意圖對比學習】

A Contrastive Sharing Model for Multi-Task Recommendation【多任務推薦的對比共享模型】

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning【使用鄰域豐富的對比學習改進圖協同過濾】

圖學習

Hypercomplex Graph Collaborative Filtering【超復雜圖協同過濾】

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning【使用鄰域豐富的對比學習改進圖協同過濾】

STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation【STAM：一種基于圖神經網絡推薦的時空聚合方法】

FIRE: Fast Incremental Recommendation with Graph Signal Processing【FIRE：使用圖信號處理的快速增量推薦】

Graph Neural Transport Networks with Non-local Attentions for Recommender Systems【用于推薦系統的具有非局部注意力的圖神經傳輸網絡】

Multi-level Recommendation Reasoning over Knowledge Graphs with Reinforcement Learning【基于強化學習的知識圖的多級推薦推理】

Revisiting Graph Neural Network based Social Recommendation【重新審視基于圖神經網絡的社交推薦】

VisGNN: Personalized Visualization Recommendation via Graph Neural Networks【VisGNN：基于圖神經網絡的個性化可視化推薦】

Path Language Modeling over Knowledge Graphs for Explainable Recommendation【可解釋推薦的知識圖路徑語言建模】

GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction【GSL4Rec：具有集體圖結構學習和下一次交互預測的基于會話的推薦】

Optimizing Rankings for Recommendation in Matching Markets【優化匹配市場中推薦的排名】Yi Su, Magd Bayoumi and Thorsten Joachims

采樣

Learning Recommenders for Implicit Feedback with Importance Resampling【通過重要性重采樣學習隱式反饋的推薦器】

A Gain-Tuning Dynamic Negative Sampler for Recommendation【用于推薦的增益調整動態負采樣器】Qiannan Zhu, Haobo Zhang, Qing He and Zhicheng Dou

新聞

FeedRec: News Feed Recommendation with Various User Feedbacks【FeedRec：具有各種用戶反饋的新聞提要推薦】

MINDSim: User Simulator for News Recommenders【MINDSim：新聞推薦者的用戶模擬器】

在線學習

Learning Neural Ranking Models Online from Implicit User Feedback【從隱式用戶反饋在線學習神經排序模型】

Efficient Online Learning to Rank for Sequential Music Recommendation【高效的在線學習對順序音樂推薦進行排名】

強化學習

Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation【基于多項選擇題的會話推薦多興趣策略學習】

Off-policy Learning over Heterogeneous Information for Recommendation【用于推薦的異構信息的異策略學習】

Multi-level Recommendation Reasoning over Knowledge Graphs with Reinforcement Learning【基于強化學習的知識圖的多級推薦推理】

其他

Learning Probabilistic Box Embeddings for Effective and Efficient Ranking【學習用于有效和高效排名的概率框嵌入】

Conditional Generation Net for Medication Recommendation【藥物推薦的條件生成網絡】

Automating Feature Selection in Deep Recommender Systems【深度推薦系統中的自動特征選擇】

Choice of Implicit Signal Matters: Accounting for UserAspirations in Podcast Recommendations【隱式信號的選擇很重要：考慮播客推薦中的用戶愿望】

Deep Unified Representation for Heterogeneous Recommendation【異構推薦的深度統一表示】

Learning to Augment for Casual User Recommendation【學習增強臨時用戶推薦】

Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation【模態匹配模態：預訓練模態分離商品表征以進行推薦】

Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems【推薦系統的相互正則化雙協同變分自動編碼器】

Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation【Re4：學習重新對比、重新參與、重新構建多興趣推薦】

Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering【一類協同過濾的異構目標的共識學習】

Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering【用于協同過濾的具有倒置多索引的快速變分自動編碼器】

HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization【HRCF：通過雙曲幾何正則化增強協同過濾】

MCL: Mixed-Centric Loss for Collaborative Filtering【MCL：用于協同過濾的混合中心損失】

Stochastic-Expert Variational Autoencoder for Collaborative Filtering【用于協同過濾的隨機專家變分自動編碼器】

Rewiring what-to-watch-next Recommendations to Reduce Radicalization Pathways【重新制定下一步觀看的建議以減少激進化途徑】

Recommendation Unlearning

What to Watch Next: Two-side Interactive Networksfor Live Broadcast Recommendation【接下來看什么：直播推薦的雙邊互動網絡】

參考

總結

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