Personalized Multimedia Item and Key Frame Recommendation

Authors: Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 find 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 λ1 = 0.001 reaches the best performance.