This paper proposes a unified generative multi-task framework that can solve multiple ABSA tasks by controlling the type of task prompts consisting of multiple element prompts.
Experiments show that the proposed method is more efficient for delayed feedback compared to various other approaches and is robust in different settings.
In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior.
To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews.
More importantly, DADNN utilizes a single model for multiple scenes which saves a lot of offline training and online serving resources.
Relevance has significant impact on user experience and business profit for e-commerce search platform.
First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.
Neural dialogue response generation has gained much popularity in recent years.
In addition, feature importance for the purpose of CTR/CVR predictions differs from one category to another.
Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.
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.
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.
To tackle this challenge, we propose a deep hierarchical reinforcement learning based recommendation framework, which consists of two components, i. e., high-level agent and low-level agent.