Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
Authors: Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, Qi Liu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments on a real-world dataset to verify the feasibility of our proposed framework. We first introduce the experimental setup, followed by the experiment results. |
| Researcher Affiliation | Collaboration | Min Hou1,2 , Le Wu3 , Enhong Chen1,2 , Zhi Li1,2 , Vincent W. Zheng4 and Qi Liu1,2 1Anhui Province Key Lab. of Big Data Analysis and Application, University of S&T of China 2School of Data Science, University of S&T of China 3Hefei University of Technology 4We Bank |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate our methods on a real-world e-commerce dataset, i.e., Amazon Fashion. This dataset was introduced in [He and Mc Auley, 2016b; Kang et al., 2017] and consist of reviews of clothing items crawled from Amazon.com. ... We combine the UT-Zap50K shoes2 dataset and the Tianchi Apparel3 dataset, which contains 50,025 shoe and over 180,000 apparel image-level attribute annotations respectively. 2http://vision.cs.utexas.edu/projects/finegrained/utzap50k/ 3https://tianchi.aliyun.com/competition/entrance/231671/information |
| Dataset Splits | Yes | For each user, we randomly select one record for validation and another one for test. |
| Hardware Specification | Yes | All experiments are trained with NVIDIA K80 graphics card and implemented by Tensorflow. |
| Software Dependencies | No | The paper mentions "Tensorflow" but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | For all the models (except Random and Pop Rank), we tune hyper-parameters via grid search on the validation set, with a regularizer selected from [0, 0.0001, 0.001, 0.01, 0.1, 1], learning rate selected from [0.0001, 0.001, 0.01], and the latent feature dimension of [10, 30, 50, 100]. We use mini-batch size of 256 to train all the models until they converge. |