Probabilistic Masked Attention Networks for Explainable Sequential Recommendation
Authors: Huiyuan Chen, Kaixiong Zhou, Zhimeng Jiang, Chin-Chia Michael Yeh, Xiaoting Li, Menghai Pan, Yan Zheng, Xia Hu, Hao Yang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies on real-world benchmark datasets show that PMAN is able to improve the performance of Transformers significantly. 5 Experiment 5.1 Experimental Setup Dataset. We consider five benchmark datasets: Amazon Beauty, Amazon-Sports2, Yelp3, Movie Lens1M4, and Steam5. |
| Researcher Affiliation | Collaboration | 1Visa Research 2Rice University 3Texas A&M University |
| Pseudocode | Yes | Algorithm 1 PMAN Input: The training sequence set S, attention capacity B, embedding size d. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | Dataset. We consider five benchmark datasets: Amazon Beauty, Amazon-Sports2, Yelp3, Movie Lens1M4, and Steam5. For each dataset, we group the interactions by users, and sort their items by the timestamps ascendingly. Following [Fan et al., 2022], we adopt 5-core setting to filter out unpopular items and inactive users with fewer than five interaction records. Their statistics are listed in Table 1. |
| Dataset Splits | Yes | Following the procedure [Kang and Mc Auley, 2018; Li et al., 2020; Fan et al., 2022], we use the last item of each user s sequence for testing, the second-to-last for validation, and the remaining items for training. |
| Hardware Specification | No | The paper mentions 'with the same hardware' but does not provide specific details about the hardware used for experiments (e.g., CPU, GPU model, memory). |
| Software Dependencies | No | The paper mentions using 'Adam as optimizer' but does not specify version numbers for any software dependencies or libraries (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | The parameters for the baselines are initialized as their original settings and are then carefully tuned to obtain optimal performance. We adopt Adam as optimizer and search embedding dimension d in Eq. (2) within {32, 64, 128}, the length of item sequence n within {25, 50}. For the attention capacity B in Problem (8), we vary the ratio r in {0.3, 0.5, 0.7, 0.9}, such that B = r n2. Moreover, all of our PMANs only use single-head attention in the experiments. |