no code implementations • EMNLP 2020 • Haiyan Wu, Ying Liu, Shaoyun Shi
In existing works, aggregating on a syntax tree usually considers local information of sub-trees.
no code implementations • 27 Dec 2021 • Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu, Yongfeng Zhang
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.
1 code implementation • CIKM 2021 • Shaoyun Shi
News recommendation plays an indispensable role in acquiring daily news for users.
2 code implementations • 11 Jun 2021 • Bin Hao, Min Zhang, Weizhi Ma, Shaoyun Shi, Xinxing Yu, Houzhi Shan, Yiqun Liu, Shaoping Ma
To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.
no code implementations • 9 Jan 2021 • Hanxiong Chen, Xu Chen, Shaoyun Shi, Yongfeng Zhang
Motivated by this problem, we propose to generate free-text natural language explanations for personalized recommendation.
3 code implementations • 20 Aug 2020 • Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang
Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
3 code implementations • 16 May 2020 • Hanxiong Chen, Shaoyun Shi, Yunqi Li, Yongfeng Zhang
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i. e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions.
no code implementations • 17 Oct 2019 • Shaoyun Shi, Hanxiong Chen, Min Zhang, Yongfeng Zhang
The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning.