Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915208016953344 |
|---|---|
| author | Chen, Sirui Han, Shen Chen, Jiawei Hu, Binbin Zhou, Sheng Wang, Gang Feng, Yan Chen, Chun Wang, Can |
| author_facet | Chen, Sirui Han, Shen Chen, Jiawei Hu, Binbin Zhou, Sheng Wang, Gang Feng, Yan Chen, Chun Wang, Can |
| contents | Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of RS, this critical property is often overlooked in the design of model architectures. To address this issue, we propose Rankformer, a ranking-inspired recommendation model. The architecture of Rankformer is inspired by the gradient of the ranking objective, embodying a unique (graph) transformer architecture -- it leverages global information from all users and items to produce more informative representations and employs specific attention weights to guide the evolution of embeddings towards improved ranking performance. We further develop an acceleration algorithm for Rankformer, reducing its complexity to a linear level with respect to the number of positive instances. Extensive experimental results demonstrate that Rankformer outperforms state-of-the-art methods. The code is available at https://github.com/StupidThree/Rankformer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rankformer: A Graph Transformer for Recommendation based on Ranking Objective Chen, Sirui Han, Shen Chen, Jiawei Hu, Binbin Zhou, Sheng Wang, Gang Feng, Yan Chen, Chun Wang, Can Information Retrieval Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of RS, this critical property is often overlooked in the design of model architectures. To address this issue, we propose Rankformer, a ranking-inspired recommendation model. The architecture of Rankformer is inspired by the gradient of the ranking objective, embodying a unique (graph) transformer architecture -- it leverages global information from all users and items to produce more informative representations and employs specific attention weights to guide the evolution of embeddings towards improved ranking performance. We further develop an acceleration algorithm for Rankformer, reducing its complexity to a linear level with respect to the number of positive instances. Extensive experimental results demonstrate that Rankformer outperforms state-of-the-art methods. The code is available at https://github.com/StupidThree/Rankformer. |
| title | Rankformer: A Graph Transformer for Recommendation based on Ranking Objective |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2503.16927 |