Deep Matrix Factorization Models for Recommender Systems
Authors: Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, Jiajun Chen
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show the effectiveness of both our proposed model and the loss function. On several benchmark datasets, our model outperformed other state-of-the-art methods. We also conduct extensive experiments to evaluate the performance within different experimental settings. |
| Researcher Affiliation | Academia | Hong-Jian Xue, Xin-Yu Dai, Jianbing Zhang, Shujian Huang, Jiajun Chen National Key Laboratory for Novel Software Technology; Nanjing University, Nanjing 210023, China Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China xuehj@nlp.nju.edu.cn, {daixinyu,zjb,huangsj,chenjj}@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 DMF Training Algorithm With Normalized Cross Entropy |
| Open Source Code | No | We implemented our proposed methods based on Tensorflow3, which will be released publicly upon acceptance. |
| Open Datasets | Yes | We evaluate our models on four widely used datasets in recommender systems: Movie Lens 100K(ML100k), Movie Lens 10M(ML1m), Amazon music(Amusic), Amazon movies(Amovie). They are publicly accessible on the websites 1 2. 1https://grouplens.org/datasets/movielens/ 2http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | Yes | We held-out the latest interaction as a test item for every user and utilize the remaining dataset for training. ... To determine hyper-parameters of DMF methods, we randomly sampled one interaction for each user as the validation data and tuned hyper-parameters on it. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'TensorFlow3' but does not specify a version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | When training our models, we sampled seven negative instances per positive instance. For neural network, we randomly initialized model parameters with a Gaussian distribution (with a mean of 0 and standard deviation of 0.01), optimizing the model with mini-batch Adam [Kingma and Ba, 2014]. We set the batch size to 256, and set the learning rate to 0.0001. |