A Hybrid Bandit Framework for Diversified Recommendation
Authors: Qinxu Ding, Yong Liu, Chunyan Miao, Fei Cheng, Haihong Tang4036-4044
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed LMDB framework in balancing the recommendation accuracy and diversity. |
| Researcher Affiliation | Collaboration | Qinxu Ding,1 Yong Liu,1,2 Chunyan Miao,3 Fei Cheng,4 Haihong Tang4 1 Alibaba-NTU Singapore Joint Research Institute 2 Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) 3School of Computer Science and Engineering, Nanyang Technological University 4Alibaba Group |
| Pseudocode | Yes | Algorithm 1 Modular Dispersion Greedy Search |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The experiments are performed on the following datasets: Movielens-100K, Movielens-1M1, and Anime2. 1https://grouplens.org/datasets/movielens/ 2https://www.kaggle.com/Cooper Union/animerecommendations-database |
| Dataset Splits | Yes | Firstly, we randomly split each dataset into two non-overlapping sets. Specifically, 80% of the users are used for training, and the remaining 20% users are used for testing. |
| Hardware Specification | No | The paper describes the experiments but does not provide specific details about the hardware (e.g., GPU or CPU models, memory) used for running them. |
| Software Dependencies | No | The paper mentions the use of BPRMF but does not specify version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | Empirically, we set the dimensionality of the item embeddings to 10. [...] We set the size of At to 10 for all methods. The hyper-parameter settings for each method are summarized in Appendix. |