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..
Bridging Recommendation and Marketing via Recurrent Intensity Modeling
Authors: Yifei Ma, Ge Liu, Anoop Deoras
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments where we use marketing as an alternative to coldstart item exploration, by setting a minimal-exposure constraint for every item in the audience base.Our experiments are available at https://github.com/ awslabs/recurrent-intensity-model-experiments |
| Researcher Affiliation | Industry | Yifei Ma, Ge Liu & Anoop Deoras AWS AI Labs EMAIL |
| Pseudocode | Yes | Algorithm 1 Dual planner for online (and offline) matching |
| Open Source Code | Yes | Our experiments are available at https://github.com/ awslabs/recurrent-intensity-model-experiments |
| Open Datasets | Yes | Movielens (ML) (Harper & Konstan, 2015)..., Netflix (NF)2... 2https://www.kaggle.com/netflix-inc/netflix-prize-data, Yoochoose (YC)3... 3https://www.kaggle.com/phhasian0710/yoochoose |
| Dataset Splits | Yes | We hold out time windows only on the test users (Group-B in Table S1)... All training users (Group A) and the observed histories of the testing users (Group B left part) are considered training data.RNN-HP and GCMC(*) require further splitting of the training set. On NF, we create a set-back window between [T , T) from all users and on ML/YC, we keep the same time [T, T + T) but change the user base to Group A for validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'tick software package', 'Implicit package', and 'Light FM package' but does not specify their version numbers. |
| Experiment Setup | Yes | We fix α = 1% in equation 9 and vary 0 β 1% as item min-exposure constraints.Algorithm 1 Dual planner for online (and offline) matching Require: λxy = λ(x, y), ideally scaled to λ 1; user-capacity α = K/Y; item-constraint β; user-state distribution P(X) from a past period of time; step-size γ... init ˆvy Unif( 1, 0), y Y for k in [0, 1, . . . , 100] do set ϵ = 0.8k; |