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 | Conference PDF | Archive PDF | Plain Text | 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]. |