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..
Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation
Authors: Guibing Guo, Shichang Ouyang, Xiaodong He, Fajie Yuan, Xiaohua Liu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a series of experiments on four real datasets, and show that even a basic model can be greatly enhanced with the involvement of DIB and PEB in terms of ranking accuracy. |
| Researcher Affiliation | Collaboration | 1Northeastern University, China 2JD AI Research, Beijing, China 3Tencent, Shenzhen, China |
| Pseudocode | No | The paper describes methods using natural language and diagrams, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and datasets can be obtained from https://github.com/ouououououou/DIB-PEBSequential-RS |
| Open Datasets | Yes | We conduct our experiments on four real-word datasets, including three Amazon datasets1 [He and Mc Auley, 2016b; Mc Auley et al., 2015] and Movie Lens-100K2. 1http://jmcauley.ucsd.edu/data/amazon/ 2https://grouplens.org/datasets/movielens/100k/ |
| Dataset Splits | Yes | For each user, we preserve the last two interactions to validation and testing sets, while the rest interactions are used for training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' and 'Adam optimizer' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For each method, the grid search is applied to ο¬nd the optimal settings of hyperparameters using the validation set. These include embedding dimensions d from {16, 32, 50, 100, 150} and the learning rate from {0.001, 0.002, 0.005, 0.1, 0.2, 1}. For RUMI, Caser, MN-DIB, GRU-DIB and GRU4Rec, the sequence length L is from {3, 5, 10, 15, 20}. For MN-DIB and GRU-DIB, the window size of latest similar users is chosen from {3, 5, 10, 15}. To compare each loss function fairly, the sampling number of BPR, TOP1, NCE and PEB is set to 25. |