Heterogeneous Collaborative Filtering

TL;DR

This paper introduces Heterogeneous Collaborative Filtering (HCF), a novel recommendation approach designed to address cold start and content diversity issues in social networks, improving recommendation quality.

Contribution

The paper proposes HCF, a new collaborative filtering method that incorporates content heterogeneity to enhance recommendation diversity and mitigate cold start problems.

Findings

HCF improves recommendation quality in real-world social networks.

HCF effectively addresses cold start issues.

HCF enhances content diversity in recommendations.

Abstract

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.