XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System

1 code implementation • 24 Oct 2020 • Fuyu Lv, Mengxue Li, Tonglei Guo, Changlong Yu, Fei Sun, Taiwei Jin, Wilfred Ng

The offline experimental results based on real-world E-commerce data demonstrate the effectiveness and verify the importance of unclicked items in sequential recommendation.

Metric Learning Retrieval +1

Enhanced Transport Distance for Unsupervised Domain Adaptation

no code implementations • CVPR 2020 • Mengxue Li, Yi-Ming Zhai, You-Wei Luo, Peng-Fei Ge, Chuan-Xian Ren

Unsupervised domain adaptation (UDA) is a representative problem in transfer learning, which aims to improve the classification performance on an unlabeled target domain by exploiting discriminant information from a labeled source domain.

Transfer Learning Unsupervised Domain Adaptation

Using Argument-based Features to Predict and Analyse Review Helpfulness

no code implementations • EMNLP 2017 • Haijing Liu, Yang Gao, Pin Lv, Mengxue Li, Shiqiang Geng, Minglan Li, Hao Wang

We study the helpful product reviews identification problem in this paper.

Crowdsourcing Argumentation Structures in Chinese Hotel Reviews

no code implementations • 5 May 2017 • Mengxue Li, Shiqiang Geng, Yang Gao, Haijing Liu, Hao Wang

Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts.