Interaction-Aware Factorization Machines for Recommender Systems
Authors: Fuxing Hong, Dongbo Huang, Ge Chen3804-3811
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Fuxing Hong, Dongbo Huang, Ge Chen Advertising and Marketing Services, Corporate Development Group, Tencent Inc. cstur4@zju.edu.cn, {andrewhuang,gechen}@tencent.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate our models on two real-world datasets, Movie Lens3(Harper and Konstan 2015) and Frappe(Baltrunas et al. 2015), for personalized tag recommendation and context-aware recommendation. |
| Dataset Splits | Yes | The datasets are divided into a training set (70%), a probe set (20%), and a test set (10%). All models are trained on the training set, and the optimal parameters are obtained on the held-out probe set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The embedding size of features is set to 256, and the batch size is set to 4096 and 128 for Movie Lens and Frappe, respectively. We also pre-train the feature embeddings with FM to get better results. For IFM and INN, we set τ = 10 and tune the other hyperparameters on the probe set. We use L2 regularization, dropout, and early stopping. |