  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.

Information Retrieval