# Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

arXiv: https://arxiv.org/abs/1412.7610 · HTML: https://mlsanity.com/paper/1412.7610/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2014.

## Built on by (most cited first, 4 of 4)

- [All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction](https://mlsanity.com/paper/2403.17740/) (2024)
- [Rating and aspect-based opinion graph embeddings for explainable recommendations](https://mlsanity.com/paper/2107.03385/) (2021)
- [Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations](https://mlsanity.com/paper/2107.03226/) (2021)
- [Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems](https://mlsanity.com/paper/2311.06323/) (2023)

## Abstract (arXiv)

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-based recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algorithms have considered using multiple types of relations within one social network. In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on two real-world data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) show the effectiveness and efficiency of our algorithm.