Augmented Degree Correction for Bipartite Networks with Applications to Recommender Systems
| Title: | Augmented Degree Correction for Bipartite Networks with Applications to Recommender Systems |
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| Authors: | Leinwand, Benjamin, Pipiras, Vladas |
| Source: | Appl Netw Sci 9, 19 (2024) |
| Publication Year: | 2023 |
| Collection: | Computer Science Statistics |
| Subject Terms: | Computer Science - Social and Information Networks, Statistics - Applications |
| More Details: | 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. Comment: 21 pages, 4 figures |
| Document Type: | Working Paper |
| DOI: | 10.1007/s41109-024-00630-6 |
| Access URL: | http://arxiv.org/abs/2311.06436 |
| Accession Number: | edsarx.2311.06436 |
| Database: | arXiv |
| DOI: | 10.1007/s41109-024-00630-6 |
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