Near-Neighbor Methods in Random Preference Completion
Authors: Ao Liu, Qiong Wu, Zhenming Liu, Lirong Xia4336-4343
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data verify our theoretical findings, and demonstrate that our algorithm is robust in highdim spaces. Experiments on Netflix data shows that our anchor-based algorithm is superior to the KT-k NN algorithm and a standard collaborative filter (using the cosinesimilarities to determine neighbors). |
| Researcher Affiliation | Academia | Ao Liu Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180, USA liua6@rpi.edu Qiong Wu, Zhenming Liu Department of Computer Science College of William and Mary Williamsburg, VA 23187, USA qwu05@email.wm.edu and zliu@cs.wm.edu Lirong Xia Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180, USA xial@cs.rpi.edu |
| Pseudocode | Yes | Algorithm 1: KT-k NN (it produces incorrect results) and Algorithm 2: Anchor-k NN. |
| Open Source Code | No | The paper does not contain any statement or link indicating the availability of its source code. |
| Open Datasets | Yes | We examine the performance of Anchork NN using the standard Netflix dataset (?; ?). |
| Dataset Splits | No | The paper mentions choosing an "optimal k (chosen by using cross-validations for Ground-Truth-k NN)" but does not specify the splits for training, validation, or testing for its own experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions using an "optimal k (chosen by using cross-validations for Ground-Truth-k NN)" and refers to "different k [101, 1601]" and "k = 751" for Ground-truth kNN in experiments. However, it does not provide explicit hyperparameters like learning rates, batch sizes, or other training configurations. |