Estimating Propensity for Causality-based Recommendation without Exposure Data
Authors: Zhongzhou Liu, Yuan Fang, Min Wu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate PROPCARE through both quantitative and qualitative experiments. |
| Researcher Affiliation | Academia | Zhongzhou Liu School of Computing and Information Systems Singapore Management University Singapore, 178902 zzliu.2020@phdcs.smu.edu.sg Yuan Fang School of Computing and Information Systems Singapore Management University Singapore, 178902 yfang@smu.edu.sg Min Wu Institute for Infocomm Research A*STAR Singapore, 138632 wumin@i2r.a-star.edu.sg |
| Pseudocode | Yes | Algorithm 1: Training PROPCARE Input: Observed training interaction data D. Output: Model parameters Θ. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement for the open-sourcing of the code for PROPCARE, its main contribution. It only mentions using and implementing baselines, some of which have links to their respective codebases. |
| Open Datasets | Yes | We employ three standard causality-based recommendation benchmarks. Among them, DH_original and DH_personalized are two versions of the Dunn Humby dataset [30]... The third dataset is Movie Lens 100K (ML) [29]... The raw data are available at https://www.dunnhumby.com/careers/engineering/sourcefiles. ... The raw data are available at https://grouplens.org/datasets/movielens. |
| Dataset Splits | Yes | On each dataset, we generate the training/validation/test sets following their original work [30, 29], respectively. ... For the DH datasets, the data generation process is repeated 10 times to simulate the 10-week training data, once more to simulate the 1-week validation data, and 10 more times to simulate the 10-week testing data. |
| Hardware Specification | Yes | All experiments were conducted on a Linux server with a AMD EPYC 7742 64-Core CPU, 512 GB DDR4 memory and four RTX 3090 GPUs. |
| Software Dependencies | Yes | We implement PROPCARE using TensorFlow 2.11 in Python 3.10. |
| Experiment Setup | Yes | Specifically, in PROPCARE, the trade-off parameter λ and µ are set to 10 and 0.4, respectively, on all datasets. ... where the threshold ϵ is set to 0.2 for DH_original and DH_personalized, and 0.15 for ML. ... For DH_original and DH_personalized, the scaling factor c is set to 0.8, while for ML it is set to 0.2. ... The embedding model fe takes (xu||xi) as input and is implemented as an MLP with 256, 128 and 64 neurons for its layers. fp and fr are both implemented as MLPs with 64, 32, 16, 8 neurons for the hidden layers and an output layer activated by the sigmoid function. ... PROPCARE is trained with a stochastic gradient descent optimizer using mini-batches, with a batch size set to 5096. |