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Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems
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| Published in: | ArXiv cs.IR Recent Papers |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| _version_ | 1864764977698570249 |
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| collection | WordPress RSS FRELIP Feed Integration |
| container_title | ArXiv cs.IR Recent Papers |
| description | |
| discipline_display | Library & Information Science |
| discipline_facet | Library & Information Science |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:50340 |
| institution | FRELIP |
| journal_source_facet | ArXiv cs.IR Recent Papers |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems ArXiv cs.IR Recent Papers Library Science Library & Information Science |
| sub_discipline_display | Library Science |
| sub_discipline_facet | Library Science |
| subject_display | ArXiv cs.IR Recent Papers Library Science Library & Information Science ArXiv cs.IR Recent Papers Library Science Library & Information Science |
| subject_facet | ArXiv cs.IR Recent Papers Library Science Library & Information Science |
| title | Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems |
| title_auth | Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems |
| title_full | Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems |
| title_fullStr | Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems |
| title_full_unstemmed | Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems |
| title_short | Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems |
| title_sort | light-fmp: lightweight feature and model pruning for enhanced deep recommender systems |
| topic | ArXiv cs.IR Recent Papers Library Science Library & Information Science |
| url | https://arxiv.org/abs/2605.06441v1 |