Embedded Files
Multi-task Ranking in Recommendations
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1. Recommending What Video to Watch Next: A Multitask Ranking System (paper)
2. Soft Parameter Sharing - Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (paper)
3. Hard Parameter Sharing - Deep Neural Networks for YouTube Recommendations: (paper)
4. An Overview of Multi-Task Learning in Deep Neural Networks (paper)
5. Task Clustering and Gating for Bayesian Multitask Learning (paper)
6. Why I like it: Multi-task Learning for Recommendation and Explanation. (paper)
7. Multi-task Learning for Recommender Systems (paper)
Selection Bias in Learning to rank
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1. Learning to Rank with Selection Bias in Personal Search (paper)
2. Evaluating the Accuracy of Implicit Feedback from
Clicks and Query Reformulations in Web Search (paper)
3. Unbiased Learning-to-Rank with Biased Feedback. (paper)
4. Batch Learning from Logged Bandit Feedback through
Counterfactual Risk Minimization (paper)
Papers from WSDM 2020
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1. JNET: Learning User Representations via Joint NetworkEmbedding and Topic Embedding (paper)
2. Learning a Joint Search and Recommendation Model from User-Item Interactions (paper)
3. List wise Learning to Rank by Exploring Unique Ratings (paper)
4. Product Knowledge Graph Embedding for E-commerce (paper)
5. Sequential Modeling of Hierarchical User Intention andPreference for Next-item Recommendation (paper)
6. Sequential Recommendation with Dual SideNeighbor-based Collaborative Relation Modeling (paper)
7. Time Interval Aware Self-Aention for SequentialRecommendation (paper)
8. End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding (paper)
9. Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations (paper)
Sequential Recommendation
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1. A Dynamic Co-attention Network for Session-based Recommendation. CIKM'2019. (paper)
2. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. CIKM'2019. (paper)
3. Hierarchical Gating Networks for Sequential Recommendation. KDD2019. (paper)(code)
4. Hierarchical Context enabled Recurrent Neural Network for Recommendation. AAAI2019. (paper)(code)
5. Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction. SIGIR'19. (paper)(code)
6. Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems. WWW'19. (paper)
7. A Simple Convolutional Generative Network for Next Item Recommendation. WSDM'19. (paper)(code)
8. Sequential Variational Autoencoders for Collaborative Filtering. WSDM'19. (paper)
9. Session-based Recommendation with Graph Neural Networks. AAAI'19. (paper)(code)
10. Self-Attentive Sequential Recommendation. ICDM'18. (paper)(code)
11. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. WSDM'18. (paper)(code)
12. Latent Cross: Making Use of Context in Recurrent Recommender Systems. WSDM'18. (paper)
13. Sequential Recommendation with User Memory Networks. WSDM'18. (paper)
14. STAMP: Short-Term A ention/Memory Priority Model for Session-based Recommendation. KDD'18. (paper)(code)
15. Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. CIKM'18. (paper)
16. Translation-based recommendation. RecSys'17. (paper)
17. Neural Attentive Session-based Recommendation. CIKM'17. (paper)
18. Neural Survival Recommender. WSDM'17. (paper)
19. Recurrent recommender networks. WSDM'17. (paper)
20. Improved Recurrent Neural Networks for Session-based Recommendations. arxiv'16. (paper)
21. Session-based Recommendations with Recurrent Neural Networks. ICLR'16. (paper)(code-Theano, code-TensorFlow)
22. Fusing similarity models with markov chains for sparse sequential recommendation. ICDM'16. (paper)
23. Dynamic Poisson Factorization. RecSys'15. (paper)
24. Factorizing personalized markov chains for next-basket recommendation. WWW'10. (paper)
25. Temporal collaborative filtering with bayesian probabilistic tensor factorization. SIAM'10. (paper)
26. Collaborative Filtering with Temporal Dynamics. KDD'09. (paper)
27. Blog explaining attention (blog)
Papers from Recsys 2019
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1. Collective Embedding for Nueral Context-Aware Recommender(paper)
2. Are We Really Making Much Progress? A Worrying Analysis of Recent Nueral Recommendation Approaches (paper)
3. Deep Generative Ranking for Personalized Recommendation (paper)
4. Revisiting Online Personal Search Metrics With the User in Mind (paper)
5. A Recommender System for Heterogeneous and Time Sensistive Environment (paper)
6. Deep Context-Aware Recommender System Utilizing Sequential Latent Context (paper)
Deep Models for Recommendations
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1. Applying Deep Learning to Airbnb Search (paper)
2. Deep Neural Networks for YouTube Recommendations (paper)
3. On application of learning to rank for e-commerce search (paper)
4. Practical lessons from predicting clicks on ads at facebook (paper)
5. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (paper)
6. Amazon.com Recommendations Item-to-Item Collaborative Filtering (paper)
7. Tutorial: Deep Learning for Matching in Search and Recommendation (paper)
8. Personalized Re-ranking for Recommendation (paper)
9.Combining Decision Trees and Neural Networks for
Learning-to-Rank in Personal Search (paper)
Deep Models for Candidate Set Generations
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1. ***Attention Modeling Seminal Paper:*** NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE (paper)
2. Multi-Label Learning with Millions of Labels: Recommending Advertiser Bid Phrases for Web Pages (paper)
3. Learning Tree-based Deep Model for Recommender Systems (paper)
[Code]
4. On Using Very Large Target Vocabulary for Neural Machine Translation (paper)
General DNN
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1. Understanding the difficulty of training deep feedforward neural networks (paper)
Machine Translation
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1. A method for automatic evaluation of machine translation. (paper)
Revenue Optimizations In Recommendations
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1. ValuePick: Towards a Value-Oriented Dual-Goal Recommender System (paper)
2. Show Me the Money: Dynamic Recommendations for Revenue Maximization (paper)
3. Recomended for You: The Impact of Profit Incentives on the Relevance of Online Recommendations (paper)
4. Maximizing profit using recommender systems (paper)
5. Customized Regression Model for Airbnb Dynamic Pricing (paper)
Graph Embedding In Recommendations
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1. LINE: Large-scale Information Network Embedding (paper)
2. Recommender Systems with Social Regularization (paper)
Knowledge Graph-based Recommendation
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1. Explainable Knowledge Graph-based Recommendation via Deep Reinforcement Learning. arXiv'19. (paper)
2. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. SIGIR'19. (paper)
3. Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems. TOIS'19. (paper)
4. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization. KDD'19. (paper)(code)
5. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference. WWW'19. (paper)
6. Jointly Learning Explainable Rules for Recommendation with Knowledge Graph. WWW'19. (paper)
7. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. WWW'19. (paper)
8. Explainable Reasoning over Knowledge Graph Paths for Recommendation. AAAI'19. (paper)
9. Heterogeneous Information Network Embedding for Recommendation. TKDE'18. (paper)(code)
10. Leveraging Meta-path based Context for Top-N Recommendation with A Neural Co-Attention Model. KDD'18. (paper)(code)
11. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. CIKM'18. (paper)(code)
12. DKN: Deep Knowledge-Aware Network for News Recommendation. WWW'18. (paper)
13. SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction. WSDM'18. ([paper](https://dl.acm.org/citation.cfm?doid=3159652.3159666))
14. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Arxiv'18. (paper)
15. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. KDD'17. (paper)(code)
16. Collaborative Knowledge Base Embedding for Recommender Systems. KDD'16. (paper)
17. Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach.RecSys'16. (paper)
18. Personalized Entity Recommendation: A Heterogeneous Information Network Approach. WSDM'14. (paper)
19. PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks.VLDB'11.(paper)
User Behavior Modeling In Recommendations
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1. Self-Attentive Sequential Recommendation (paper)
2. Micro Behaviors: A New Perspective in E-commerce Recommender Systems (paper)
3. Life-stage Prediction for Product Recommendation in E-commerce (paper)
4. Attention Is All You Need (paper)
Other Top-K Recommendations Papers
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1. SLIM: Sparse Linear Methods for Top-N Recommender Systems (paper)
Session Based and Sequence Models for Recommendations
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1. [Adaptive User Modeling with Long and Short-Term Preferences for Personalized
2. [Improved Recurrent Neural Networks for Session-based Recommendations]
3. [Incorporating Dwell Time in Session-Based Recommendations with Recurrent Neural Networks]
4. [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks]
5. [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations]
6. [RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation]
7. [SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS]
8. [Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks]
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