Efficient Thompson Sampling for Online Matrix-Factorization Recommendation
Authors: Jaya Kawale, Hung H. Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments in collaborative filtering using several real-world datasets demonstrate that PTS significantly outperforms the current state-of-the-arts. |
| Researcher Affiliation | Collaboration | Jaya Kawale, Hung Bui, Branislav Kveton Adobe Research San Jose, CA {kawale, hubui, kveton}@adobe.com Long Tran Thanh University of Southampton Southampton, UK ltt08r@ecs.soton.ac.uk Sanjay Chawla Qatar Computing Research Institute, Qatar University of Sydney, Australia sanjay.chawla@sydney.edu.au |
| Pseudocode | Yes | Algorithm 1 Particle Thompson Sampling for Matrix Factorization (PTS) |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or explicitly state that the code is publicly available. |
| Open Datasets | Yes | We use five real world datasets as follows: Movielens 100k, Movielens 1M, Yahoo Music4, Book crossing5 and Each Movie as shown in Table 1. ... 4http://webscope.sandbox.yahoo.com/ 5http://www.bookcrossing.com |
| Dataset Splits | No | We ran the algorithm using 80% data for training and the rest for testing and computed the MSE by averaging the results over 5 runs. The paper specifies a training and testing split, but does not mention a separate validation set or split. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'PMF implementation by [5]' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We use K = 2 for all the algorithms and use 30 particles for our approach. For PTS we set the value of σ2 = 0.5 and σ2 u = 1, σ2 v = 1. For PTS-B (Bayesian version, see Algo. 1 for more details), we set σ2 = 0.5 and the initial shape parameters of the Gamma distribution as α = 2 and β = 0.5. For both ICF-20 and ICF-50, we set σ2 = 0.5 and σ2 u = 1. ... We use the stochastic gradient descent to update the latent factors with a mini-batch size of 50. In order to make a recommendation, we use the ϵ-greedy strategy and recommend the highest Ui V T with a probability ϵ and make a random recommendations otherwise. (ϵ is set as 0.95 in our experiments.) |