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 | Venue 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 EMAIL Kun Jin Google EMAIL Andrew Estornell Byte Dance EMAIL Xiaoying Zhang Byte Dance EMAIL Yiling Chen Harvard University EMAIL Yang Liu University of California, Santa Cruz EMAIL |
| 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. |