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..
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Authors: Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan, Xiangnan He
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effectiveness of Dream Rec through extensive experiments and comparisons with existing methods. |
| Researcher Affiliation | Academia | University of Science and Technology of China The Hong Kong Polytechnic University |
| Pseudocode | Yes | Algorithm 1 Training phase of Dream Rec Algorithm 2 Generation phase of Dream Rec |
| Open Source Code | Yes | Codes and data are opensourced at https://github.com/YangZhengyi98/DreamRec. |
| Open Datasets | Yes | We use three datasets from real-world sequential recommendation scenarios: Yoo Choose, Kuai Rec, and Zhihu (the statistics of datasets are illustrated in Appendix B): Yoo Choose dataset comes from Rec Sys Challenge 2015 4. Kuai Rec [42] dataset is collected from the recommendation logs of a video-sharing mobile app. Zhihu [43] dataset is collected from a socialized knowledge-sharing community. |
| Dataset Splits | Yes | For all datasets, we first sort all sequences in chronological order, and then split the data into training, validation and testing data at the ratio of 8:1:1. |
| Hardware Specification | Yes | We implement all models with Python 3.7 and Py Torch 1.12.1 in Nvidia GeForce RTX 3090. |
| Software Dependencies | Yes | We implement all models with Python 3.7 and Py Torch 1.12.1 in Nvidia GeForce RTX 3090. |
| Experiment Setup | Yes | The embedding dimension of items is fixed as 64 across all models. The learning rate is tuned in the range of [0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005]. For our Dream Rec, we fix the unconditional training probability pu as 0.1 suggested by [36]. We search the total diffusion step T in the range of [50, 100, 200, 500, 1000, 2000], and the personalized guidance strength w in the range of [0, 2, 4, 6, 8, 10]. |