Top-N Recommender System via Matrix Completion
Authors: Zhao Kang, Chong Peng, Qiang Cheng
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level. |
| Researcher Affiliation | Academia | Zhao Kang, Chong Peng, Qiang Cheng Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, USA |
| Pseudocode | Yes | Algorithm 1 Solve (3) |
| Open Source Code | Yes | The implementation of our method is available at: https://github.com/sckangz/recom_mc. |
| Open Datasets | Yes | We evaluate the performance of our method on six different real datasets whose characteristics are summarized in Table 1. These datasets are from different sources and at different sparsity levels. They can be broadly categorized into two classes. |
| Dataset Splits | Yes | We employ 5-fold Cross-Validation to demonstrate the efficacy of our proposed approach. |
| Hardware Specification | Yes | The time is measured on the same machine with an Intel Xeon E3-1240 3.40GHz CPU that has 4 cores and 8GB memory, running Ubuntu and Matlab (R2014a). |
| Software Dependencies | Yes | Matlab (R2014a) |
| Experiment Setup | Yes | The parameters for each method are as follows: ... Our: auxiliary parameters μ0 and γ. We show the effects of different initializations μ0 and γ on HR on dataset Delicious in Figure 2. |