Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchical Negative Binomial Factorization for Recommender Systems on Implicit Feedback
Authors: Li-Yen Kuo, Ming-Syan Chen4181-4188
AAAI 2021 | Venue PDF | 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. |