Diversified Interactive Recommendation with Implicit Feedback
Authors: Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang, Binqiang Zhao, Haihong Tang4932-4939
AAAI 2020 | 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 method in balancing the recommendation accuracy and diversity. |
| Researcher Affiliation | Collaboration | 1Alibaba-NTU Singapore Joint Research Institute 2Joint NTU-UBC Research Centre of Excellence in Active Living in the Elderly (LILY) 3School of Computer Science and Engineering, Nanyang Technological University 4University of Science and Technology of China, 5Alibaba Group |
| Pseudocode | Yes | Algorithm 1 Thompson Sampling for DC2B; Algorithm 2 DPP Greedy Search S O( θ, Xt) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for their proposed method. |
| Open Datasets | Yes | Movielens-100K, Movielens-1M1, and Anime2. ... 1https://grouplens.org/datasets/movielens/ 2https://www.kaggle.com/Cooper Union/animerecommendations-database |
| Dataset Splits | No | A validation set is sampled from training data to choose hyper-parameters. (This mentions a validation set but does not provide specific details on its size or how it's sampled, making its split not fully reproducible.) |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions using BPRMF for embeddings but does not list any specific software dependencies with version numbers (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | Empirically, we set the dimensionality of the item embeddings to 10. ... N is set to 10, 30, and 50. ... For all methods, we empirically set the size of St to 10 in each trial. ... α is set to 0.9 for MMR. ϵ is set to 0.1 for ϵ-Greedy, and θ is set to 0.6 for DPPmap. In C2UCB, we set λ0 = 100, λ = 0.1, and σ = 1. In ECBandit, we set the parameter λ = 1. For DC2B, we empirically set α = 3, and λ = 1, on all datasets. |