# Graph-based Collaborative Ranking

arXiv: https://arxiv.org/abs/1604.03147 · HTML: https://mlsanity.com/paper/1604.03147/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings](https://mlsanity.com/paper/2106.04209/) (2021)
- [Attention-Based Recommendation On Graphs](https://mlsanity.com/paper/2201.05499/) (2022)
- [Ranking of Bangla Word Graph using Graph-based Ranking Algorithms](https://mlsanity.com/paper/2509.01011/) (2025)

## Abstract (arXiv)

Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in collaborative ranking domain, in which calculating the users similarities and recommending items are based on ranking data. Some graph-based approaches have been proposed to address the data sparsity problem, but they suffer from two flaws. First, they fail to correctly model the users priorities, and second, they cannot be used when the only available data is a set of ranking instead of rating values. In this paper, we propose a novel graph-based approach, called GRank, that is designed for collaborative ranking domain. GRank can correctly model users priorities in a new tripartite graph structure, and analyze it to directly infer a recommendation list. The experimental results show a significant improvement in recommendation quality compared to the state of the art graph-based recommendation algorithms and other collaborative ranking techniques.