Alibaba - Revision history

Zeno Gantner: /* Papers */

2023-09-28T10:41:06Z

Papers

Zeno Gantner at 12:38, 19 May 2023

2023-05-19T12:38:24Z

Zeno Gantner: /* Papers */

2023-05-15T17:01:12Z

Papers

Zeno Gantner at 23:36, 2 December 2022

2022-12-02T23:36:28Z

Zeno Gantner: Created page with "'''Alibaba''' is a Chinese e-commerce company. == Papers == # {{search}} (personalised e-commerce search) [https://arxiv.org/abs/1805.08524 Globally Optimized Mutual Influen..."

2022-12-02T23:36:01Z

Created page with "'''Alibaba''' is a Chinese e-commerce company. == Papers == # {{search}} (personalised e-commerce search) [https://arxiv.org/abs/1805.08524 Globally Optimized Mutual Influen..."

New page

'''Alibaba''' is a Chinese e-commerce company.

== Papers ==

# {{search}} (personalised e-commerce 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
# [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
# {{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/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], AAAI 2019
# {{neural}} [https://arxiv.org/pdf/1905.06874.pdf Behavior Sequence Transformer for E-commerce Recommendation in Alibaba], DLP-KDD workshop 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://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
# {{ltor}} [https://ieeexplore.ieee.org/abstract/document/9495161 AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online], IEEE TKDE 2021

== Software ==

# [https://github.com/alibaba/EasyRec EasyRec]
# [https://github.com/alibaba/DeepRec DeepRec]: uses TF 1.15

== External links ==
* [https://github.com/alibaba GitHub] (436 repos as of 2022-05, at least 2 of them relevant)

[[Category:Company]]

@@ Line 7: / Line 7: @@
 # [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
 # {{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/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], [[AAAI 2019]]

@@ Line 22: / Line 22: @@
 # [https://github.com/alibaba/EasyRec EasyRec]
 # [https://github.com/alibaba/DeepRec DeepRec]: uses TF 1.15
 == External links ==

@@ Line 3: / Line 3: @@
 == 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/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
+# {{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/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], AAAI 2019
+# {{neural}} [https://arxiv.org/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], [[AAAI 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/pdf/1905.06874.pdf Behavior Sequence Transformer for E-commerce Recommendation in Alibaba], [[DLP-KDD workshop 2019]]
-# {{neural}} Zhu et al.: Joint Optimization of Tree-based Index and Deep Model for Recommender Systems, NIPS 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
+# {{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://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
 == Software ==