Primarily responsible for maintaining and optimizing the foundational E-commerce search ranking model, initially used in fine-ranking and extended to recall, coarse-ranking, and long-tail scenarios.

• Model Infrastructure Maintenance (Framework and Speed Optimization):

  • Deconstrucing neural networks into configurable blocks, enabling maximum loading of pre-trained parameters when modifying features/network blocks. Data needed for model convergence after modification: Reduced from 2+ months to 1 week.
  • Shared user-side computations in each request. Offline training speed: increased from 37 to 76(+105%) samples / (cpu * sec.); Online inference runtime: Reduced from 95.5ms to 28.8ms (speed +232%).

• Algorithm:

  • Clustered Embedding Learning (CEL): Improved CTR AUC by +0.6%, reduced model size. Our research paper has been accepted at WWW23. My primary role was its implementation in our real-world ranking model, achieving a 10x speed increase.
  • Multi-task: Implemented Progressive Layered Extraction (PLE), resulting in a CTR AUC+0.5%.
  • Numeric Features Modeling: Implemented AutoDis, CTR AUC +0.2% and CVR AUC +0.3%.

• Techical Reports: 1) Practical Experiences in Rank Model Speed Optimization; 2) Survey of Feature Crosses; 3) Multi-task Modeling; 4) Long Sequence Modeling.

As a multinational e-commerce platform, AliExpress needs to pay attention to the behavioral differences of users in different countries. Hence, we designed a country-specific multitask fine-ranking model. During the internship, I reproduced 14 papers and finally adopted PLE, ESMM, and Gradient Block to optimize the multitask framework, designed country-level PLE in the top network to capture the specific information of the top 5 key countries; used DCN-v2 in the bottom network to achieve higher-order feature crossover for country-differentiated features. Results: The fine-ranking results in offline experiments achieved significant improvements in CTR AUC (+0.50%), CTR GAUC (+0.78%), L2P AUC (+0.53%), and L2P GAUC (+1.17%).

Mainly responsible for the official user search in ByteDance’s “Toutiao” series apps (Toutiao, Xigua Video, DongCheDi, etc.), including the vertical search results under the user search tab, and the user card result under the all web search and video search tab. Increase the vertical search query Click-Through-Rate (qCTR: the ratio of clicked queries) under the user tab from the previous 15% to 45% (+30%). Increase the recall rate of user cards under the integrated search and video channels by 60%, and the top 3 user card CTR in the integrated search by 5%.