Improving Top-N Recommendation with Heterogeneous Loss
Authors: Feipeng Zhao, Yuhong Guo
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed approach on a set of personalized top-N recommendation tasks. The experimental results show the proposed approach outperforms a number of state-of-the-art methods on top-N recommendation. 4 Experimental Results In this section, we first present the experimental setting and then report the empirical results. |
| Researcher Affiliation | Academia | Feipeng Zhao and Yuhong Guo Department of Computer and Information Sciences Temple University, Philadelphia, PA 19122, USA {feipeng.zhao, yuhong}@temple.edu |
| Pseudocode | Yes | Algorithm 1 Projected Stochastic Gradient Descent |
| Open Source Code | No | The paper does not provide any links or explicit statements about releasing its source code. |
| Open Datasets | Yes | Datasets. We used five datasets in our experiments: Yahoo! music ratings v1.0 dataset, Yahoo! movie ratings v1.0 dataset, Movie Lens 100k (ml-100k) dataset, Movie Lens 1M (ml-1m) dataset and Netflix dataset. ... For ml-1m and Netflix we used the same datasets as [Aiolli, 2014]. |
| Dataset Splits | No | For each dataset, we split it into a training set and a test set. For each user, we randomly selected ten feedbacks and placed them into the test set and the rest were used as training set. No mention of a separate validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Parameters Selection. For the proposed method, we have two parameters, and β. We selected the weight control parameter from {10, 100, 1000, 5000, 10000} and selected the regularization parameter β from {0.001, 0.01, 0.1, 1, 10, 100, 1000}. For WRMF, the latent factor dimension f is selected from {10, 20, 50, 100, 200, 500, 1000} and the regularization parameter λ is selected from {0.1, 1, 10, 100, 1000}. |