Hybrid Item-Item Recommendation via Semi-Parametric Embedding
Authors: Peng Hu, Rong Du, Yao Hu, Nan Li
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of the proposed method. We conduct experiments on real-world datasets, and experimental results show the effectiveness of the proposed method, especially for the cold-start items. The experimental section is organized as follows. We first describe datasets, evaluation metric and baseline algorithms used to verify the superiority of SPE for I2I problems. Then detail results are summarized and that is followed by a section to get deeper insights into the idea about how SPE model balances the parametric and non-parametric part adaptively. Finally, we show robust results of the extended model. |
| Researcher Affiliation | Industry | Peng Hu , Rong Du , Yao Hu and Nan Li Alibaba Group, Hang Zhou, China {sylar.hp, qingzhao.dr, yaoohu, nanli.ln}@alibaba-inc.com |
| Pseudocode | No | The paper describes the proposed model and optimization process using mathematical equations and diagrams, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The algorithms are evaluated on three real-world datasets: Amazon product data [Mc Auley et al., 2015] (Specifically the musical instruments subset), Yelp1 and a private dataset collected from Alibaba s second-hand e-commerce platform. For Alibaba private dataset, we collect items from one of the most popular categories for the convenience of calculation. As Amazon product data and Yelp originally contain user-item interactions data, we follow [Grbovic et al., 2015] and convert the dataset into relationships between items for I2I recommendation. https://www.yelp.com/dataset |
| Dataset Splits | No | The paper mentions negative sampling ratios and evaluation strategies but does not provide specific percentages or counts for training, validation, and test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | When comparing the performance of different models, common hyperparameters like the embedding dimension k and the negative sampling ratio r are set to the same value for fairness (if not specified, k is set to 16, and the positive/negative sampling ratio is set to 1 : 3 by default). We use grid search to find the best value of the trade-off hyperparameters for Neu MF, CDL and our proposed models. |