Search Results for author:
Found 10 papers, 4 papers with code
Automatic Generation of Product-Image Sequence in E-commerce
For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images.
TeKo: Text-Rich Graph Neural Networks with External Knowledge
Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).
Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided Masking
Players of online games generate rich behavioral data during gaming.
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.
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System
In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms.
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.
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.
Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews
TSPRA combines topics (i. e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework.
