Search Results for author: Yue Shang

Found 10 papers, 4 papers with code

Automatic Generation of Product-Image Sequence in E-commerce

1 code implementation26 Jun 2022 Xiaochuan Fan, Chi Zhang, Yong Yang, Yue Shang, Xueying Zhang, Zhen He, Yun Xiao, Bo Long, Lingfei Wu

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

no code implementations15 Jun 2022 Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao, Jiawei Han, Lingfei Wu

Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).

DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization

no code implementations SIGIR 2021 Xueying Zhang, Yunjiang Jiang, Yue Shang, Zhaomeng Cheng, Chi Zhang, Xiaochuan Fan, Yun Xiao, Bo Long

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.

Text Generation

RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System

1 code implementation18 Oct 2021 Kai Wang, Zhene Zou, Yue Shang, Qilin Deng, Minghao Zhao, Yile Liang, Runze Wu, Jianrong Tao, Xudong Shen, Tangjie Lyu, Changjie Fan

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.

Combinatorial Optimization reinforcement-learning +2

A unified Neural Network Approach to E-CommerceRelevance Learning

no code implementations26 Apr 2021 Yunjiang Jiang, Yue Shang, Rui Li, Wen-Yun Yang, Guoyu Tang, Chaoyi Ma, Yun Xiao, Eric Zhao

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.

Information Retrieval Retrieval

Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

no code implementations13 Jan 2021 Ziyang Liu, Zhaomeng Cheng, Yunjiang Jiang, Yue Shang, Wei Xiong, Sulong Xu, Bo Long, Di Jin

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.

Network Embedding

Fine-tune BERT for E-commerce Non-Default Search Ranking

no code implementations21 Aug 2020 Yunjiang Jiang, Yue Shang, Hongwei Shen, Wen-Yun Yang, Yun Xiao

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

1 code implementation19 Dec 2018 Zheng Chen, Yong Zhang, Yue Shang, Xiaohua Hu

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.

Collaborative Filtering Online Review Rating +2

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