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Found 8 papers, 1 papers with code
Residual Multi-Task Learner for Applied Ranking
ResFlow is now fully deployed in the pre-rank module of Shopee Search.
Clustered Embedding Learning for Recommender Systems
However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up.
Exploit Customer Life-time Value with Memoryless Experiments
As a measure of the long-term contribution produced by customers in a service or product relationship, life-time value, or LTV, can more comprehensively find the optimal strategy for service delivery.
A General Traffic Shaping Protocol in E-Commerce
Our algorithm can straightforwardly optimize the linear programming in the prime space, and its solution can be simply applied by a stochastic strategy to fulfill the optimized objective and the constraints in expectation.
Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System
The homepage recommendation on most E-commerce applications places items in a hierarchical manner, where different channels display items in different styles.
Learning-To-Ensemble by Contextual Rank Aggregation in E-Commerce
Combined with the idea of Bayesian Optimization and gradient descent, we solve the online contextual Black-Box Optimization task that finds the optimal weights for sub-models given a chosen RA model.
Imitate TheWorld: A Search Engine Simulation Platform
Recent E-commerce applications benefit from the growth of deep learning techniques.
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator that ensures the generalization of the evaluator.
