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..
Learning to Recommend from Sparse Data via Generative User Feedback
Authors: Wenlin Wang4436-4444
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed framework is able to enrich the learning of user preference and boost the performance of existing collaborative filtering methods on multiple datasets. |
| Researcher Affiliation | Academia | Wenlin Wang Department of Electrical and Computer Engineering, Duke University EMAIL |
| Pseudocode | No | The paper describes the learning algorithm in text and mathematical formulations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We investigate the effectiveness of the proposed CF-SFL framework on three benchmark datasets of recommendation systems. (i) Movie Lens-20M (ML-20M)... (ii) Netflix-Prize (Netflix)... (Bennett, Lanning et al. 2007); (iii) Million Song Dataset (MSD)... (Bertin-Mahieux et al. 2011). |
| Dataset Splits | Yes | Figure 3: Performance (NDCG@100) boost on the validation sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer' but does not specify any software names with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | To learn the model, we pre-train the recommender (150 epochs for ML-20M and 75 epochs for Netflix and MSD) and optimize the entire framework (50 epochs for ML-20M and 25 epochs for the other two). ℓ2 regularization with a penalty term 0.01 is applied to the recommender, and Adam optimizer (Kingma and Ba 2014) with batch in size of 500 is employed. |