Augmented Degree Correction for Bipartite Networks with Applications to Recommender Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914839576707072 |
|---|---|
| author | Leinwand, Benjamin Pipiras, Vladas |
| author_facet | Leinwand, Benjamin Pipiras, Vladas |
| contents | In recommender systems, users rate items, and are subsequently served other product recommendations based on these ratings. Even though users usually rate a tiny percentage of the available items, the system tries to estimate unobserved preferences by finding similarities across users and across items. In this work, we treat the observed ratings data as partially observed, dense, weighted, bipartite networks. For a class of systems without outside information, we adapt an approach developed for dense, weighted networks to account for unobserved edges and the bipartite nature of the problem. This approach allows for community structure, and for local estimation of flexible patterns of ratings across different pairs of communities. We compare the performance of our proposed approach to existing methods on a simulated data set, as well as on a data set of joke ratings, examining model performance in both cases at differing levels of sparsity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_06436 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Augmented Degree Correction for Bipartite Networks with Applications to Recommender Systems Leinwand, Benjamin Pipiras, Vladas Social and Information Networks Applications In recommender systems, users rate items, and are subsequently served other product recommendations based on these ratings. Even though users usually rate a tiny percentage of the available items, the system tries to estimate unobserved preferences by finding similarities across users and across items. In this work, we treat the observed ratings data as partially observed, dense, weighted, bipartite networks. For a class of systems without outside information, we adapt an approach developed for dense, weighted networks to account for unobserved edges and the bipartite nature of the problem. This approach allows for community structure, and for local estimation of flexible patterns of ratings across different pairs of communities. We compare the performance of our proposed approach to existing methods on a simulated data set, as well as on a data set of joke ratings, examining model performance in both cases at differing levels of sparsity. |
| title | Augmented Degree Correction for Bipartite Networks with Applications to Recommender Systems |
| topic | Social and Information Networks Applications |
| url | https://arxiv.org/abs/2311.06436 |