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Found 8 papers, 2 papers with code
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce
An effective ranking model should give a personalized ranking list for each user according to the user preference.
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products.
Sequential Search with Off-Policy Reinforcement Learning
Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.
A unified Neural Network Approach to E-CommerceRelevance Learning
We describe a highly-scalable feed-forward neural model to provide relevance score for (query, item) pairs, using only user query and item title as features, and both user click feedback as well as limited human ratings as labels.
From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search
We introduce deep learning models to the two most important stages in product search at JD. com, one of the largest e-commerce platforms in the world.
Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
In addition, feature importance for the purpose of CTR/CVR predictions differs from one category to another.
Clustering tweets usingWikipedia concepts
We address the polysemy issue with a Bayesian model, and the synonymy issue by exploiting the Wikipedia redirections.
CLTC: A Chinese-English Cross-lingual Topic Corpus
A preliminary evaluation with CLTC corpus indicates that the corpus is effective in evaluating cross-lingual topic detection methods.
