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..
Recommendation by Mining Multiple User Behaviors with Group Sparsity
Authors: Ting Yuan, Jian Cheng, Xi Zhang, Shuang Qiu, Hanqing Lu
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the real-world dataset demonstrate that our model can integrate users multiple types of behaviors into recommendation better, compared with other state-of-the-arts. |
| Researcher Affiliation | Academia | Ting Yuan, Jian Cheng, Xi Zhang, Shuang Qiu, Hanqing Lu National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Science EMAIL |
| Pseudocode | Yes | Algorithm 1 Optimization Algorithm for GSMF |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | Yes | To evaluate our model s recommendation quality, we crawled the dataset from the publicly available website Douban1 ... 1http://www.douban.com |
| Dataset Splits | No | The paper mentions splitting data into training and testing sets (e.g., '80% of the data from each types of the behaviors for training and the rest for testing'), but does not explicitly state the use of a separate validation set for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameter values of our GSMF are: b = 1 (b = 1, 2, 3, 4), λ = 0.05 for the three training sets. β = 70 for 80% and 60% training sets, and β = 40 for 40% training set. |