Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Top-N Recommender System via Matrix Completion
Authors: Zhao Kang, Chong Peng, Qiang Cheng
AAAI 2016 | Venue PDF | 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. |