Search Results for author: Shaoyun Shi

Found 6 papers, 3 papers with code

A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing

1 code implementation11 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.

Gender Prediction

Generate Natural Language Explanations for Recommendation

no code implementations9 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.

Denoising Recommendation Systems

Neural Logic Reasoning

1 code implementation20 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.

Recommendation Systems

Neural Collaborative Reasoning

3 code implementations16 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.

Collaborative Filtering Decision Making +2

Neural Logic Networks

no code implementations17 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.

Collaborative Filtering

Cannot find the paper you are looking for? You can Submit a new open access paper.