Research map / Learning theory and algorithms / Matrix and tensor methods
Top-N Recommender System via Matrix Completion
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · Delicious | HR: 0.360 -> 0.382 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · Delicious | ARHR: 0.227 -> 0.241 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · lastfm | HR: 0.187 -> 0.206 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · lastfm | ARHR: 0.105 -> 0.113 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · BX | HR: 0.052 -> 0.065 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · BX | ARHR: 0.031 -> 0.043 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · ML100K | HR: 0.397 -> 0.428 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · ML100K | ARHR: 0.207 -> 0.215 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · Netflix | HR: 0.196 -> 0.226 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · Netflix | ARHR: 0.111 -> 0.127 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · Yahoo | HR: 0.334 -> 0.367 | Helped |
| Use nonconvex log-determinant matrix completion instead of LorSLIM | Top-N recommendation · Yahoo | ARHR: 0.191 -> 0.218 | Helped |
Closest papers by meaning
- Optimal Discrete Matrix Completion (AAAI 2016 · similarity 0.87)
- Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization (ICML 2025 · similarity 0.87)
- Mixture Matrix Completion (NeurIPS 2018 · similarity 0.87)
- Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation (ICML 2024 · similarity 0.85)
- Online Matrix Completion: A Collaborative Approach with Hott Items (ICML 2024 · similarity 0.85)
- From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking (AAAI 2025 · similarity 0.85)
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