An Estimation and Analysis Framework for the Rasch Model
Authors: Andrew Lan, Mung Chiang, Christoph Studer
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now experimentally demonstrate the efficacy of the proposed framework. First, we use synthetically generated data to numerically compare our L-MMSE-based upper bound on the MSE of the PM estimator to the widely-used lower bound based on Fisher information (Zhang et al., 2011; Yang et al., 2012). We then use several real-world collaborative filtering datasets to show that the L-MMSE estimator achieves comparable predictive performance to that of the PM and MAP estimators. |
| Researcher Affiliation | Academia | Department of Electrical Engineering, Princeton University, Purdue University, School of Electrical and Computer Engineering, Cornell University. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | ML, a processed version of the ml-100k dataset from the Movielens project (Herlocker et al., 1999) |
| Dataset Splits | Yes | We evaluate the prediction performance of the L-MMSE, MAP, PM, and Logit-MAP estimators using ten-fold cross validation. We randomly divide the entire dataset into ten equally-partitioned folds (of user-item response pairs), leave out one fold as the held-out testing set and use the other folds as the training set. We tune the prior variance parameter σ2 x using a separate validation set (one fold in the training set). |
| Hardware Specification | No | The paper mentions 'on a standard laptop computer' when discussing computational efficiency, but does not provide specific hardware details such as CPU/GPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper describes the methods used (e.g., 'standard Gibbs sampling procedure'), but it does not specify any software names with version numbers or list software dependencies with specific versions. |
| Experiment Setup | Yes | We vary the number of users U {20, 50, 100} and the number of items Q {20, 50, 100, 200}. We generate the user ability and item difficulty parameters from zero-mean Gaussian distributions with variance σ2 x = σ2 a = σ2 d. We vary σ2 x so that the signalto-noise ratio (SNR) corresponds to { 10, 0, 10} decibels (d B). and We tune the prior variance parameter σ2 x using a separate validation set (one fold in the training set). |