Neural Utility Functions
Authors: Porter Jenkins, Ahmad Farag, J. Stockton Jenkins, Huaxiu Yao, Suhang Wang, Zhenhui Li7917-7925
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that Neural Utility Functions can recover theoretical item relationships better than vanilla neural networks, analytically show existing neural networks are not quasi-concave and do not inherently reason about trade-offs, and that augmenting existing models with a utility loss function improves recommendation results. The Neural Utility Functions we propose are theoretically motivated, and yield strong empirical results. |
| Researcher Affiliation | Academia | Porter Jenkins 1, Ahmad Farag 2, J. Stockton Jenkins 3, Huaxiu Yao 1, Suhang Wang 1, Zhenhui Li 1 1Pennsylvania State University 2Georgia Tech University 3Brigham Young University |
| Pseudocode | No | The paper includes a 'Training Procedure' section and Figure 1 illustrating the process, but it does not contain a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | For code see https://github.com/porterjenkins/neural-utilityfunctions |
| Open Datasets | Yes | We also evaluate recommendation performance on the Movielens 25M (Harper and Konstan 2015) and Amazon 18 (Mc Auley et al. 2015) datasets. |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., '80% of the samples are allocated for training and 20% for testing'), but it does not explicitly mention a separate validation dataset split with specific percentages or counts. |
| Hardware Specification | Yes | All models were implemented in Pytorch (Paszke et al. 2019) and trained on a Google Deep Learning VM with 60 GB of RAM and two Tesla K80 GPU s. |
| Software Dependencies | No | The paper mentions 'Pytorch (Paszke et al. 2019)' as the implementation framework but does not provide a specific version number for Pytorch or any other software dependencies used. |
| Experiment Setup | Yes | We train all models using the Adam optimizer (Kingma and Lei Ba 2014). We select k = 5 for the size of the complement and supplement sets. All models were implemented in Pytorch (Paszke et al. 2019) and trained on a Google Deep Learning VM with 60 GB of RAM and two Tesla K80 GPU s. We train each model multiple times to estimate the variance. |