Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty
Authors: Junyang Jiang, Deqing Yang, Yanghua Xiao, Chenlu Shen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments on two benchmark datasets not only justify that our proposed Gaussian embeddings capture the uncertainty of users very well, but also demonstrate its superior performance over the state-of-the-art recommendation models. |
| Researcher Affiliation | Academia | 1School of Computer Science, Shanghai Key Laboratory of Data Science, Fudan University, China 2School of Data Science, Fudan University, China 3Shanghai Institute of Intelligent Electronics & Systems, China {jiangjy15, yangdeqing, shawyh, clshen17}@fudan.edu.cn |
| Pseudocode | No | The paper provides a framework overview diagram (Figure 2) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code package of implementing our framework is published on https://github.com/Junyang Jiang/gaussian-recommender. |
| Open Datasets | Yes | We evaluated our models on two public benchmark datasets: Movie Lens 1M (ml-1m)1,and Amazon music (Music)2. ... 1https://grouplens.org/datasets/movielens/ 2http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | Yes | Following [He et al., 2018b; 2017], we adopted the leave-one-out evaluation. We held out the latest one interaction of each user as the positive sample in test set, and paired it with 99 items randomly sampled from unobserved interactions. For each positive sample of every user in training set, we randomly sampled 4 negative samples. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions using 'Adam algorithm' but does not provide specific version numbers for any software dependencies or libraries (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Table 2 displays our framework s performance of movie recommendation under different hyper-parameter settings. ... And we set K = 9 in the following comparison experiments according to the results in Table 2. In addition, D is also set to 64. |