https://github.com/xiangwang1223/neural_social_collaborative_ranking

Item Silk Road: Recommending Items from Information Domains to Social Users, SIGIR2017
https://github.com/xiangwang1223/neural_social_collaborative_ranking

cross-domain-recommendation neural-collaborative-filtering sigir2017 social-recommendation

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Item Silk Road: Recommending Items from Information Domains to Social Users, SIGIR2017

• Host: GitHub
• URL: https://github.com/xiangwang1223/neural_social_collaborative_ranking
• Owner: xiangwang1223
• Created: 2018-05-18T06:50:37.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-29T21:25:50.000Z (over 5 years ago)
• Last Synced: 2024-01-07T01:41:07.115Z (10 months ago)
• Topics: cross-domain-recommendation, neural-collaborative-filtering, sigir2017, social-recommendation
• Language: Python
• Homepage:
• Size: 10.7 KB
• Stars: 38
• Watchers: 1
• Forks: 15
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Neural Social Collaborative Ranking

This is our Tensorflow implementation for the paper:

>Xiang Wang, Xiangnan He, Liqiang Nie, and Tat-Seng Chua (2019). [Item Silk Road: Recommending Items from Information Domains to Social Users](https://dl.acm.org/citation.cfm?id=3080771). In SIGIR ’17, Shinjuku, Tokyo, Japan, August 07-11, 2017.

Author: Dr. Xiang Wang (xiangwang at u.nus.edu)

## Introduction

Neural Social Collaborative Ranking (NSCR) is a new recommendation framework which seamlessly sews up the user-item interactions in the recommendation scenarios and user-user connections in social networks, in order to recommend items to potential social users.

## Citation 

If you want to use our codes in your research, please cite:

```

@inproceedings{NSCR17,

  author    = {Xiang Wang and

               Xiangnan He and

               Liqiang Nie and

               Tat{-}Seng Chua},

  title     = {Item Silk Road: Recommending Items from Information Domains to Social

               Users},

  booktitle = {{SIGIR}},

  pages     = {185--194},

  year      = {2017},

}

```