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..
A Boosting Algorithm for Item Recommendation with Implicit Feedback
Authors: Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao
IJCAI 2015 | Venue PDF | 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 Proο¬le 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 Ο 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. |