Exploiting Music Play Sequence for Music Recommendation
Authors: Zhiyong Cheng, Jialie Shen, Lei Zhu, Mohan Kankanhalli, Liqiang Nie
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two public datasets demonstrate that our methods could significantly improve the performance on both rating prediction and topn recommendation tasks. |
| Researcher Affiliation | Academia | 1School of Computing, National University of Singapore 2Department of Computer and Information Sciences, Northumbria University, UK 3School of Information Technology & Electrical Engineering, The University of Queensland 4School of Computer Science and Technology, Shandong University, China |
| Pseudocode | No | The paper provides mathematical equations for the model but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Dataset Two publicly accessible datasets are used for evaluation: Last.fm-1k music4 [Celma Herrada, 2009] and 30Music dataset5 [Turrin et al., 2015]. 4http://www.dtic.upf.edu/ ocelma/Music Recommendation Dataset/ lastfm-1K.html 5http://recsys.deib.polimi.it/?page id=54 |
| Dataset Splits | Yes | The reported results are the average values gained over 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for the experiments. |
| Software Dependencies | No | The paper mentions techniques like 'skip-gram negative sampling' but does not specify any software libraries or their version numbers (e.g., PyTorch 1.9). |
| Experiment Setup | Yes | In the song similarity computation, we set the size of feature vector to 100 in skip-gram negative sampling, and set the window size to 5. The results are obtained based on the parameter settings: d = 128, α = 0.5, and k = 5, where d is the number of factors in matrix factorization. |