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..
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning
Authors: Romain WARLOP, Alessandro Lazaric, Jérémie Mary
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically validate the model assumptions and the algorithm in a number of realistic scenarios. |
| Researcher Affiliation | Collaboration | Romain Warlop fifty-five, Paris, France Seque L Team, Inria Lille, France EMAIL Alessandro Lazaric Facebook AI Research Paris, France EMAIL Jérémie Mary Criteo AI Lab Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1 The LINUCRL algorithm. |
| Open Source Code | No | The paper states that a dataset "will be released publicly as soon as possible" but makes no such statement regarding the source code for the methodology. |
| Open Datasets | Yes | In order to provide a preliminary validation of our model, we use the movielens-100k dataset [9, 7]. |
| Dataset Splits | No | The paper describes using the movielens-100k dataset to estimate model parameters and construct a simulator, but does not specify explicit train/validation/test splits for the data. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We choose K = 10 actions corresponding to different genres of movies, and we set d = 5 and w = 5, which results into Kw = 10^5 states. [...] The parameters that describe the dependency of the reward function on the recency (i.e., θ j,a) are computed by using the ratings averaged over all users for each state encountered and for ten different genres in the dataset. [...] Finally, the observed reward is obtained by adding a small random Gaussian noise to the linear function. |