Paper is open resoursed at follow: https://ieeexplore.ieee.org/document/8795572
This is the project corresponding to the paper "Incremental Community Detection and Group-Specific Model for Cold-Start Recommendation", the results and experiments are applied on computer with an Intel E5-2603 CPU. It is noticeable that the bipartite community detection initialization method is run by an C++ application.
If you have any questions, please contact me by email: cs_xcy@126.com
Besides, I am looking for a PhD position in 2020 fall. I would be really appreciated if you could provide or recommend any opportunities.
| Package | Version |
|---|---|
| NumPy | 1.15.4 |
| Pandas | 0.23.4 |
| python-igraph | 0.7 |
| biLouvain | - |
We provide a python script to run a demo experiment.
python script/demo.py
If you want to test on your own dataset, best to make sure the ID of users and items continuous and starts from 1. A good example of usage can be found in the any files located in scripts folder.
| Folder | Features |
|---|---|
| out_groups | The proposed incremental community detection methods. |
| core | Some central codes includes ALS and LSE algorithms. |
| data | Extracted experimental data. |
| model | A framework with the whole process. |
| scripts | The entrance of experiments. |
| tools | Some functions that are often used. |
C. Xue, S. Wu, Q. Zhang and F. Shao, "An Incremental Group-Specific Framework Based on Community Detection for Cold Start Recommendation," in IEEE Access, vol. 7, pp. 112363-112374, 2019. doi: 10.1109/ACCESS.2019.2935090 keywords: {recommender systems;social networking (online);incremental group-specific framework;cold start recommendation;cold start problem;rating information;recommender systems;decoupled normalization method;incremental community detection methods;incremental group-specific model;incremental data;Recommender systems;Collaboration;Complex networks;Data models;Computer science;Predictive models;Recommender systems;complex networks;incremental community detection;cold start}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8795572&isnumber=8600701