Difference between revisions of "Alibaba"
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== Papers == | == Papers == | ||
| − | # {{search}} [https://arxiv.org/abs/1805.08524 Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search], IJCAI 2018 | + | # {{search}} [https://arxiv.org/abs/1805.08524 Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search], [[IJCAI 2018]] |
| − | # {{performance}} Wang et al.: [https://arxiv.org/pdf/1803.02349.pdf Billion-scale commodity embedding for e-commerce recommendation in Alibaba], KDD 2018 | + | # {{performance}} Wang et al.: [https://arxiv.org/pdf/1803.02349.pdf Billion-scale commodity embedding for e-commerce recommendation in Alibaba], [[KDD 2018]] |
# [https://arxiv.org/pdf/1801.02294.pdf Learning Tree-based Deep Model for Recommender Systems], KDD 2018 | # [https://arxiv.org/pdf/1801.02294.pdf Learning Tree-based Deep Model for Recommender Systems], KDD 2018 | ||
# [https://arxiv.org/abs/1706.06978 Deep Interest Network for Click-Through Rate Prediction], KDD 2018 | # [https://arxiv.org/abs/1706.06978 Deep Interest Network for Click-Through Rate Prediction], KDD 2018 | ||
| − | # {{search}} {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3298689.3347000?download=true Personalized Re-ranking for Recommendation], RecSys 2019 | + | # [https://arxiv.org/pdf/1905.09248.pdf Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction], KDD 2019 |
| − | # {{neural}} [https://arxiv.org/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], AAAI 2019 | + | # {{search}} {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3298689.3347000?download=true Personalized Re-ranking for Recommendation], [[RecSys 2019]] |
| − | # {{neural}} [https://arxiv.org/pdf/1905.06874.pdf Behavior Sequence Transformer for E-commerce Recommendation in Alibaba], DLP-KDD workshop 2019 | + | # {{neural}} [https://arxiv.org/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], [[AAAI 2019]] |
| − | # {{neural}} Zhu et al.: Joint Optimization of Tree-based Index and Deep Model for Recommender Systems, NIPS 2019 | + | # {{neural}} [https://arxiv.org/pdf/1905.06874.pdf Behavior Sequence Transformer for E-commerce Recommendation in Alibaba], [[DLP-KDD workshop 2019]] |
| − | # {{neural}} [https://dl.acm.org/doi/abs/10.1145/3357384.3357895 BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer], CIKM 2019 | + | # {{neural}} Zhu et al.: Joint Optimization of Tree-based Index and Deep Model for Recommender Systems, [[NIPS 2019]] |
| + | # {{neural}} [https://dl.acm.org/doi/abs/10.1145/3357384.3357895 BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer], [[CIKM 2019]] | ||
# [https://ieeexplore.ieee.org/abstract/document/9495161 Co-Displayed Items Aware List Recommendation], IEEE TODO, 2020 | # [https://ieeexplore.ieee.org/abstract/document/9495161 Co-Displayed Items Aware List Recommendation], IEEE TODO, 2020 | ||
| − | # [https://dl.acm.org/doi/10.1145/3383313.3412238 PURS: Personalized Unexpected Recommender System for Improving User Satisfaction], RecSys 2020 | + | # [https://dl.acm.org/doi/10.1145/3383313.3412238 PURS: Personalized Unexpected Recommender System for Improving User Satisfaction], [[RecSys 2020]] |
| − | # [https://www.kdd.org/kdd2020/accepted-papers/view/privileged-features-distillation-at-taobao-recommendations Privileged Features Distillation at Taobao Recommendations], KDD 2020 | + | # [https://www.kdd.org/kdd2020/accepted-papers/view/privileged-features-distillation-at-taobao-recommendations Privileged Features Distillation at Taobao Recommendations], [[KDD 2020]] |
# {{ltor}} [https://ieeexplore.ieee.org/abstract/document/9495161 AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online], IEEE TKDE 2021 | # {{ltor}} [https://ieeexplore.ieee.org/abstract/document/9495161 AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online], IEEE TKDE 2021 | ||
| + | # {{ads}} [https://dl.acm.org/doi/abs/10.1145/3447548.3467086 Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling], [[KDD 2021]] | ||
== Software == | == Software == | ||
| Line 21: | Line 23: | ||
# [https://github.com/alibaba/EasyRec EasyRec] | # [https://github.com/alibaba/EasyRec EasyRec] | ||
# [https://github.com/alibaba/DeepRec DeepRec]: uses TF 1.15 | # [https://github.com/alibaba/DeepRec DeepRec]: uses TF 1.15 | ||
| + | # [https://github.com/shenweichen/DeepCTR-Torch DeepCTR] (by an Alibaba employee) | ||
| + | # [https://github.com/shenweichen/DeepCTR-Torch DeepCTR-Torch] (by an Alibaba employee) | ||
== External links == | == External links == | ||
Latest revision as of 06:41, 28 September 2023
Alibaba is a Chinese e-commerce company.
Papers
- 🔍 Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search, IJCAI 2018
- 🏃 Wang et al.: Billion-scale commodity embedding for e-commerce recommendation in Alibaba, KDD 2018
- Learning Tree-based Deep Model for Recommender Systems, KDD 2018
- Deep Interest Network for Click-Through Rate Prediction, KDD 2018
- Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction, KDD 2019
- 🔍 📑 Personalized Re-ranking for Recommendation, RecSys 2019
- 🧠 Deep Interest Evolution Network for Click-Through Rate Prediction, AAAI 2019
- 🧠 Behavior Sequence Transformer for E-commerce Recommendation in Alibaba, DLP-KDD workshop 2019
- 🧠 Zhu et al.: Joint Optimization of Tree-based Index and Deep Model for Recommender Systems, NIPS 2019
- 🧠 BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer, CIKM 2019
- Co-Displayed Items Aware List Recommendation, IEEE TODO, 2020
- PURS: Personalized Unexpected Recommender System for Improving User Satisfaction, RecSys 2020
- Privileged Features Distillation at Taobao Recommendations, KDD 2020
- 📑 AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online, IEEE TKDE 2021
- 💵 Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling, KDD 2021
Software
- EasyRec
- DeepRec: uses TF 1.15
- DeepCTR (by an Alibaba employee)
- DeepCTR-Torch (by an Alibaba employee)
External links
- GitHub (436 repos as of 2022-05, at least 2 of them relevant)
