Car Preference Dataset
We set up an experiment in Amazon Mechanical Turk to collect real pair-wise preferences over users. In this experiment users are presented with a choice to prefer a car over another based on their attributes. We have run two experiments with 10 and 20 cars respectively.
The preference questionnaire contains control questions that were randomly selected from the preferences with reversed order. These control questions were included to measure the consistency of the answers provided. In the dataset, we include all the information collected from the experiments. The reader may choose to exclude information based on the control questions which are indicators for random responses from that user.
The data collected from two experiments is formatted in three csv files respectively representing user attributes, item attributes, and preferences of users over items. In the preference sets for the given user, the first item (indicated by its ID) is preferred to the second one. If the preference was collected as a control question it is indicated by 1 otherwise 0.
We restrict our experiments to US users only to collect more unified preferences. The following information were collected from the participants and included in the dataset:
For each user, an ID (in the first column) and the number of correctly answered control questions (in the last column) is also included in the user data files.
In this experiment, we used 10 items and generated all 45 possible preferences accordingly. We performed this experiment in two shots collecting the data from 40 and 20 users separately. Cars with following attributes were presented to the participants in the first experiment:
In the second experiment, we used 20 items and randomly generated 5 subsets of 38 preferences for each user. In the contrary to the previous case, from each user only a sparse preference set was then collected. Cars with following attributes were presented to the participants in the second experiment:
You are welcome to use the data in this page for research, however please acknowledge its use with a citation:
Learning Community-based Preferences via Dirichlet Process Mixtures of Gaussian Processes, E. Abbasnejad, S. Sanner, E. V. Bonilla, P. Poupart, In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013. Beijing, China.
For inquiries or bug report please contact Ehsan Abbasnejad: ehsan (dot) abbasnejad (at) nicta (dot) com (dot) au.