For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images.
Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).
Players of online games generate rich behavioral data during gaming.
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.
Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting sequential recommendations to multi-step decision-making tasks.
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.
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.
Relevance has significant impact on user experience and business profit for e-commerce search platform.
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.
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.