Learning Nonsymmetric Determinantal Point Processes
Authors: Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on synthetic and real-world datasets, demonstrating improved predictive performance compared to symmetric DPPs, which have previously shown strong performance on modeling tasks associated with these datasets. We run extensive experiments on several synthetic and real-world datasets. We compare the performance of all methods using a standard recommender system metric: mean percentile rank (MPR). Table 1 shows the results of our performance evaluation on the Amazon and UK datasets. |
| Researcher Affiliation | Collaboration | Mike Gartrell Criteo AI Lab m.gartrell@criteo.com Victor-Emmanuel Brunel ENSAE Paris Tech victor.emmanuel.brunel@ensae.fr Elvis Dohmatob Criteo AI Lab e.dohmatob@criteo.com Syrine Krichene Criteo AI Lab syrinekrichene@google.com |
| Pseudocode | No | The paper describes algorithmic steps but does not include any formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our code is available at https://github.com/cgartrel/nonsymmetric-DPP-learning |
| Open Datasets | Yes | Amazon Baby Registries: This public dataset... UK Retail: This is a public dataset [8] that contains 25,898 baskets... |
| Dataset Splits | Yes | For all experiments, a random selection of 80% of the baskets are used for training, and the remaining 20% are used for testing. We use a small held-out validation set for tracking convergence and tuning hyperparameters. |
| Hardware Specification | No | The paper mentions implementing models and optimization algorithms but does not specify any particular hardware (e.g., GPU/CPU models, memory) used for the experiments. |
| Software Dependencies | Yes | We implement our models using Py Torch 2, and use the Adam [16] optimization algorithm to train our models. |
| Experiment Setup | Yes | Numerically, to prevent such singularities, in our implementation we add a small I correction to each LYi when optimizing Eq. 12 (we set = 10 5 in our experiments). We use a small held-out validation set for tracking convergence and tuning hyperparameters. Convergence is reached during training when the relative change in validation log-likelihood is below a pre-determined threshold, which is set identically for all models. We use D = 30, = 0 for both Amazon datasets; D0 = 100 for the Amazon 3-category dataset; D0 = 30 for the Amazon apparel dataset; D = 100, D0 = 20, = 1 for the UK dataset; and β = γ = 0 for all datasets. |