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..
Factorization Bandits for Interactive Recommendation
Authors: Huazheng Wang, Qingyun Wu, Hongning Wang
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentations on both simulations and large-scale real-world datasets confirmed the advantages of the proposed algorithm compared with several state-of-the-art factorization-based and bandit-based collaborative filtering methods. |
| Researcher Affiliation | Academia | Huazheng Wang, Qingyun Wu, Hongning Wang Department of Computer Science University of Virginia, Charlottesville VA, 22904 USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Factor UCB |
| Open Source Code | No | The paper does not provide an unambiguous statement of releasing source code or a direct link to a code repository. |
| Open Datasets | Yes | Yahoo dataset: This data set contains 10-days clickstream logs from Yahoo! Today Module collected in May 2009, totalling 45,811,883 user visits (Li et al. 2010). Last FM dataset: This dataset is extracted from the online music streaming service Last.fm (http://www.last.fm). It contains 1,892 users, 17,632 items (artists), and the users social network graph. |
| Dataset Splits | No | The paper describes splitting users into 'learning group' and 'testing group' but does not explicitly mention a 'validation' set or specific train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for replication. |
| Experiment Setup | Yes | We fixed the dimension d of observable features to 20, the dimension l of latent item factors to 5, user size N to 100, the standard derivation σ of Gaussian noise to 0.1, and the item pool size K to 1000 in our simulation. ... in our empirical evaluations the algorithm s performance is not sensitive to this setting. ... As a result, in all our following experiments we will use the manually set αu t and αa t. |