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..
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
Authors: Jiaqi Yang, De-Chuan Zhan
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our analysis on public datasets, and experimental performance confirms the effectiveness of our method. |
| Researcher Affiliation | Academia | Jia-Qi Yang De-Chuan Zhan State Key Laboratory for Novel Software Technology Nanjing University, Nanjing, 210023, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Streaming training of GDFM; Algorithm 2 Estimating wj |
| Open Source Code | Yes | code available at https://github.com/ThyrixYang/gdfm_nips22 |
| Open Datasets | Yes | Criteo Conversion Logs^2 is collected from an online display advertising service... (https://labs.criteo.com/2013/12/conversion-logs-dataset/) and Taobao User Behavior^3 is a subset of user behaviors on Taobao... (https://tianchi.aliyun.com/dataset/dataDetail?dataId=649&userId=1&lang=en-us) |
| Dataset Splits | Yes | The datasets are split into pretraining and streaming datasets. |
| Hardware Specification | No | The paper's checklist indicates that details regarding the total amount of compute and type of resources used are in the supplementary material ('[Yes] Details in supplementary'), meaning they are not explicitly described within the main paper's text. |
| Software Dependencies | No | The paper mentions general aspects of implementation ('We use the same architecture for all the methods to ensure a fair comparison. All the methods are carefully tuned. We use α = 2, β = 1, λ = 0.01, lr = 10^-3 for GDFM.') but does not specify particular software libraries or frameworks with their version numbers (e.g., PyTorch 1.x, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | All the methods are carefully tuned. We use α = 2, β = 1, λ = 0.01, lr = 10^-3 for GDFM. The network structure and procedure to calculate the proxy feedback loss Eq. (4) used by GDFM is depicted in Figure. 1 (b). |