Hierarchical Negative Binomial Factorization for Recommender Systems on Implicit Feedback
Authors: Li-Yen Kuo, Ming-Syan Chen4181-4188
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiment shows that the proposed model outperforms state-of-the-art Poisson-based methods merely with a slight loss of inference speed. |
| Researcher Affiliation | Academia | Li-Yen Kuo, Ming-Syan Chen National Taiwan University No. 1, Sec. 4, Roosevelt Rd. Taipei 10617, Taiwan |
| Pseudocode | Yes | Algorithm 1 Updating of Fast HNBF |
| Open Source Code | Yes | Source code and supplementary materials can be downloaded at https://github.com/iankuoli/HNBF |
| Open Datasets | Yes | The statistics are shown in Table 3. The first three datasets are implicit count data. Last.fm1K... Last.fm2K... Last.fm360K... Movie Lens100K... Movie Lens1M... Movie Lens20M... Jester2... Each Movie... |
| Dataset Splits | Yes | We follow the works (Gopalan et al. 2014; Basbug and Engelhardt 2016, 2017) to randomly select 20% of nonzero entries for each dataset to be used as a test set, and randomly select 1% of the nonzeros in each dataset as a validation set. |
| Hardware Specification | Yes | The experiment is conducted on PC with Quad-Core Intel Core i5 CPU @ 1.4GHz and 16GB main memory. |
| Software Dependencies | No | The paper mentions implementing methods but does not provide specific version numbers for any software, libraries, or frameworks used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In the inference model, prior parameter (a, b, c) is set to (3, 1, 0.1) on implicit count and (0.3, 0.1, 1) on explicit ratings, respectively. ... we set (g+, h+, g0, h0) to (100, 50, 10, 108) on implicit count and (1, 1, 10, 106) on explicit rating. We empirically set h+ = min( EX+[xui]2 Var X+[θuβi], EX+[xui] 3 ) and g+ = h+ per iteration during the training phase. |