Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Coactive Learning
Authors: Pannaga Shivaswamy, Thorsten Joachims
JAIR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive empirical study demonstrates the applicability of our model and algorithms on a movie recommendation task, as well as ranking for web search. |
| Researcher Affiliation | Collaboration | Pannaga Shivaswamy EMAIL Linked In Corporation Thorsten Joachims EMAIL Department of Computer Science Cornell University |
| Pseudocode | Yes | Algorithm 1 Preference Perceptron. Algorithm 2 Batch Preference Perceptron. Algorithm 3 Generic Template for Coactive Learning Algorithms Algorithm 4 Exponentiated Preference Perceptron Algorithm 5 Convex Preference Perceptron. Algorithm 6 Second-order Preference Perceptron. |
| Open Source Code | No | No explicit statement about providing source code or a link to a repository is found in the paper. |
| Open Datasets | Yes | Our first dataset is a publicly available dataset from Yahoo! (Chapelle & Chang, 2011) for learning to rank in web-search. We used the Movie Lens dataset from grouplense.org which consists of a million ratings over 3900 movies as rated by 6040 users. |
| Dataset Splits | Yes | We randomly divided users into two equally sized sets. The first set was used to obtain a feature vector xj for each movie j using the SVD embedding method for collaborative filtering (see Bell & Koren, 2007, Eqn. (15)). For the second set of users, we then considered the problem of recommending movies... After there were more than 50 pairs in the training set, the C value was obtained via five-fold cross-validation. |
| Hardware Specification | No | No specific hardware details (like CPU, GPU models, or memory) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions, or specific solver versions) used for implementing the algorithms or running experiments. |
| Experiment Setup | Yes | The γ value in the second order perceptron was simply set to one. B was set to 100 for both the algorithms for both the datasets. [we ]i = min(0, [w ]i) 1 i m, max(0, [w ]i m) m + 1 i 2m. (22) [φe(x, y)]i = +[φ(x, y)]i 1 i m [φ(x, y)]i m m + 1 i 2m (23) |