Recommendation system is important to a content sharing/creating social network. Collaborative filtering is a widely-adopted technology in conventional recommenders, which is based on similarity between positively engaged content items involving the same users. Conventional collaborative filtering (CCF) suffers from cold start problem and narrow content diversity. We propose a new recommendation approach, heterogeneous collaborative filtering (HCF) to tackle these challenges at the root, while keeping the strength of collaborative filtering. We present two implementation algorithms of HCF for content recommendation and content dissemination. Experiment results demonstrate that our approach improve the recommendation quality in a real world social network for content creating and sharing.
@article{arxiv.1909.01727,
title = {Heterogeneous Collaborative Filtering},
author = {Yifang Liu and Zhentao Xu and Cong Hui and Yi Xuan and Jessie Chen and Yuanming Shan},
journal= {arXiv preprint arXiv:1909.01727},
year = {2019}
}