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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Personalized Multimedia Item and Key Frame Recommendation
Authors: Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results on a real-world dataset clearly show the effectiveness of our proposed model on the two recommendation tasks. |
| Researcher Affiliation | Collaboration | 1Hefei University of Technology 2The University of Arizona 3Microsoft Research |
| Pseudocode | No | The paper describes the model and optimization process using mathematical equations and text, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code open or available, nor does it include a link to a code repository. |
| Open Datasets | Yes | To this end, we crawl a large dataset from Douban 1, which is one of the most popular movie sharing websites in China. ... 1www.douban.com |
| Dataset Splits | Yes | In data splitting process, we randomly select 70% user-movie ratings for training, 10% for validation and 20% for test. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing the model in "Tensor Flow [Abadi et al., 2016]" but does not provide a specific version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | In our proposed JIFR model, we choose the collaborative latent dimension size d1 and the visual dimension size d2 in the set [16,32,64], and ο¬nd when d1 = d2 = 32 reaches the best performance. The non-linear activation function f(x) in the attention networks is set as the Re LU function. Besides, the regularization parameter is set in range [0.0001, 0.001, 0.01, 01], and Ξ»1 = 0.001 reaches the best performance. |