6 papers
A Semi-supervised Scalable Unified Framework for E-commerce Query Classification
Chunyuan Yuan, Chong Zhang, Zheng Fang +5
Query classification, including multiple subtasks such as intent and category prediction, is vital to e-commerce applications. E-commerce queries are usually short and lack context…
Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising
Zhenhui Liu, Chunyuan Yuan, Ming Pang +7
Retrieval systems primarily address the challenge of matching user queries with the most relevant advertisements, playing a crucial role in e-commerce search advertising. The diver…
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval
Ming Pang, Chunyuan Yuan, Xiaoyu He +8
Traditional sparse and dense retrieval methods struggle to leverage general world knowledge and often fail to capture the nuanced features of queries and products. With the advent…
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce
Chunyuan Yuan, Ming Pang, Zheng Fang +3
Query intent classification is an essential module for customers to find desired products on the e-commerce application quickly. Most existing query intent classification methods r…
Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search
Kun-Peng Ning, Ming Pang, Zheng Fang +6
Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.…
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization
Xuefei Jiang, Dairui Liu, Ruihai Dong
In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability. However, our findings reveal a noticeable deficiency in the c…