A Boosting Algorithm for Item Recommendation with Implicit Feedback
Authors: Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Ada BPR demonstrates its effectiveness on three datasets compared with strong baseline algorithms. |
| Researcher Affiliation | Academia | Yong Liu1,2, Peilin Zhao3, Aixin Sun2, Chunyan Miao1,2 1Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, Singapore 2School of Computer Engineering, Nanyang Technological University, Singapore 3Institute for Infocomm Research, A*STAR, Singapore |
| Pseudocode | Yes | Algorithm 1: The Ada BPR Algorithm; Algorithm 2: Component Recommender Construction |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | The experiments are conducted on three public datasets: the Movie Lens-100K, Movie Lens-1M, and the Taste Profile Subset of the Million Song Dataset (TPMSD) [Bertin Mahieux et al., 2011]. |
| Dataset Splits | No | The paper mentions validation data construction for parameter tuning but does not specify a distinct validation dataset split percentage or size as part of the main dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers. |
| Experiment Setup | Yes | For Ada BPR, the dimensionality of the latent space d is selected from {10, 30, 50} (see Section 3.2), and for the other models, d is chosen from {10, 30, 50, 70, 90}. The regularization parameters are chosen from 10[ 6 : 1 : 0] (see Eq. 11), and the optimal learning rates are selected from 2[ 10 : 1 : 0] (see Alg. 2). For GBPR, the group size is chosen from {2, 3, 4, 5} and the parameter ρ is chosen from {0.2, 0.4, 0.6, 0.8, 1.0}. The number of component models in Ada MFexp, Ada MFimp, and Ada BPR are all set to 30. All experiments are repeated for 5 times, each with a different random seed. The results reported are average of the 5 runs. |