Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Interpretable Recommendation via Attraction Modeling: Learning Multilevel Attractiveness over Multimodal Movie Contents
Authors: Liang Hu, Songlei Jian, Longbing Cao, Qingkui Chen
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,822 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,184 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show the superiority of MLAM over the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Advanced Analytics Institute, University of Technology, Sydney 2Institute of Network Computing & Io T, University of Shanghai for Science and Technology 3College of Computer, National University of Defense Technology, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 The learning procedure for a mini-batch |
| Open Source Code | Yes | The code for more detail is available at: https://github.com/rainmilk/ijcai18-mlma. |
| Open Datasets | Yes | The experiments are conducted on the real-world movie watch dataset Movie Lens 1M [Harper and Konstan, 2016]. |
| Dataset Splits | No | The paper mentions 'we randomly held out 20% user watch records as the testing set, and the remainder were served as the training set.' It does not explicitly define a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | Our model is implemented using Keras [Chollet, 2015] with TensorFlow as the backend. However, specific version numbers for Keras and TensorFlow are not provided. |
| Experiment Setup | Yes | where the parameter margin needs to be tuned over data. ... we find that α = 4 performs good through our experiments. ... where α = 2 is set throughh experiments. ... where α = 1 is set throughh experiments. |