Factorization Bandits for Interactive Recommendation
Authors: Huazheng Wang, Qingyun Wu, Hongning Wang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 {hw7ww,qw2ky,hw5x}@virginia.edu |
| 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. |