Adversarial Oracular Seq2seq Learning for Sequential Recommendation
Authors: Pengyu Zhao, Tianxiao Shui, Yuanxing Zhang, Kecheng Xiao, Kaigui Bian
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We examine the performance of AOS4Rec over RNN-based and Transformer-based recommender systems on two large datasets from real-world applications and make comparisons with state-of-the-art methods. Results indicate the accuracy and efficiency of AOS4Rec, and further analysis verifies that AOS4Rec has both robustness and practicability for real-world scenarios. |
| Researcher Affiliation | Academia | School of EECS, Peking University, Beijing, China {pengyuzhao, stx pkucs, longo, kecheng, bkg}@pku.edu.cn |
| Pseudocode | No | The paper does not contain a pseudocode block or a clearly labeled algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate the proposed AOS4Rec with the baseline methods on two datasets from real-world applications. [...] YOOCHOOSE. The YOOCHOOSE dataset contains a collection of sessions encapsulating the click events from users. [...] Movie Lens. We use Movie Lens-20M Dataset, which is a stable benchmark dataset for evaluating performance of recommender systems. We follow the same preprocessing procedure from [Kang and Mc Auley, 2018; Xu et al., 2019]. |
| Dataset Splits | Yes | We discard users and items with fewer than 4 interactions, and then split the datasets into training sets, validation sets and test sets based on the length of sequences in the datasets, where the second last 20% items of the sequence are used for validation and the last 20% items are used for testing. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments (e.g., GPU models, CPU types, or cloud computing instances with their specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow). |
| Experiment Setup | Yes | We employ grid search to find the best settings of hyper-parameters and list the details in Tab. 2. [...] Table 2: Hyper-parameter settings in AOS4Rec. learning rate 1e-3, batch size 128, beam size 5, weight-decay 12. |