Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Authors: Weiyu Cheng, Yanyan Shen, Linpeng Huang3609-3616
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on four real datasets demonstrate the superior predictive performance of AFN against the state-of-the-arts. In this section, we conduct experiments to answer the following research questions: RQ1: How do our proposed methods AFN and AFN+ perform against the state-of-the-art methods? RQ2: How does the performance of AFN vary with different settings of the hyper-parameters? RQ3: What are the learned feature orders in AFN, and can AFN find useful cross features from data? |
| Researcher Affiliation | Academia | Weiyu Cheng, Yanyan Shen, Linpeng Huang Shanghai Jiao Tong University {weiyu cheng, shenyy, lphuang}@sjtu.edu.cn |
| Pseudocode | No | The paper describes methods using equations and prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We implement our methods using Tensorflow5. 5https://github.com/Weiyu Cheng/AFN-AAAI-20 |
| Open Datasets | Yes | We conduct experiments with four publicly accessible datasets following previous works (Lian et al. 2018; He and Chua 2017): Criteo1, Avazu2, Movielens3 and Frappe4. 1http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/ 2https://www.kaggle.com/c/avazu-ctr-prediction 3https://grouplens.org/datasets/movielens/ 4http://baltrunas.info/research-menu/frappe |
| Dataset Splits | Yes | For each dataset, we randomly split the instances by 8:1:1 for training, validation and test, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Tensorflow' and 'Adam optimizer' but does not specify their version numbers or other software dependencies with version information. |
| Experiment Setup | Yes | We apply Adam with a learning rate of 0.001 and a mini-batch size of 4096. The default number of logarithmic neurons is set to 1500, 1200, 800 and 600 for Criteo, Avazu, Movielens and Frappe datasets, respectively. We use 3 hidden layers and 400 neurons per layer by default in AFN. To avoid overfitting, we perform early-stopping according to the AUC on the validation set. We set the rank of feature embeddings to 10 in all the models. |