User-Creator Feature Polarization in Recommender Systems with Dual Influence
Authors: Tao Lin, Kun Jin, Andrew Estornell, Xiaoying Zhang, Yiling Chen, Yang Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then investigate, both theoretically and empirically, approaches for mitigating polarization and promoting diversity in recommender systems. We also provide empirical results (Section 5) on both synthetic and real-world (Movie Lens) data. |
| Researcher Affiliation | Collaboration | Tao Lin Harvard University tlin@g.harvard.edu Kun Jin Google kunjin@google.com Andrew Estornell Byte Dance andrew.estornell@bytedance.com Xiaoying Zhang Byte Dance zhangxiaoying.xy@bytedance.com Yiling Chen Harvard University yiling@seas.harvard.edu Yang Liu University of California, Santa Cruz yangliu@ucsc.edu |
| Pseudocode | Yes | Algorithm 1 Real-world Recommendation with Dual Influence |
| Open Source Code | Yes | Provided in the supplemental file. |
| Open Datasets | Yes | We conduct experiments on the Movie Lens 20M dataset [19]. |
| Dataset Splits | No | The paper uses the Movie Lens 20M dataset and mentions 'train' and 'validation' in the context of the two-tower model, but it does not specify explicit training/validation/test dataset splits (e.g., percentages, sample counts, or specific predefined splits) that are needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. The NeurIPS checklist states 'Computer resources are not a limitation in our experiments.' |
| Software Dependencies | No | The paper describes the computational model and architecture (e.g., 'two-tower model') but does not specify any software dependencies with version numbers (e.g., specific Python, PyTorch, or TensorFlow versions, along with other libraries) used in the experiments. |
| Experiment Setup | Yes | The dynamics is initialized by randomly generating user and creator features on the unit sphere in Rd. We pick d = 10, number of creators n = 50, number of users m = 100. We use the softmax recommendation probability function (2). We simulate the dynamics for T = 1000 steps, repeated 100 times each with a new initialization. We choose the sign impact function g(uj, vi) = sign( uj, vi ) for creator updates. For user updates, we choose inner product f(vi, uj) = vi, uj . We set them to β = 1, ηc = ηu = 0.1, and change one parameter at a time to see its effect on the dynamics. |