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Found 11 papers, 2 papers with code
Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes
Product quantization (PQ) coupled with a space rotation, is widely used in modern approximate nearest neighbor (ANN) search systems to significantly compress the disk storage for embeddings and speed up the inner product computation.
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.
DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization
We propose a novel domain-specific generative pre-training (DS-GPT) method for text generation and apply it to the product titleand review summarization problems on E-commerce mobile display. First, we adopt a decoder-only transformer architecture, which fitswell for fine-tuning tasks by combining input and output all to-gether.
Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index
Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems.
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.
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search
We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
Relevance has significant impact on user experience and business profit for e-commerce search platform.
Fine-tune BERT for E-commerce Non-Default Search Ranking
The quality of non-default ranking on e-commerce platforms, such as based on ascending item price or descending historical sales volume, often suffers from acute relevance problems, since the irrelevant items are much easier to be exposed at the top of the ranking results.
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.
Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for the same search query.
