Coactive Learning
Authors: Pannaga Shivaswamy, Thorsten Joachims
JAIR 2015 | Venue PDF | Archive PDF | Plain Text | 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) |