Recommendation by Mining Multiple User Behaviors with Group Sparsity
Authors: Ting Yuan, Jian Cheng, Xi Zhang, Shuang Qiu, Hanqing Lu
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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 {tyuan, jcheng, xi.zhang, shuang.qiu, luhq}@nlpr.ia.ac.cn |
| 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. |