Group Recommender Systems - Some Experimental Results

Vineet Padmanabhan, Prabhu Kiran, Abdul Sattar


Recommender Systems (RS) are software applications which aim to support users in their decision making while interacting with large information spaces. Most recommender systems are designed for recommending items to individuals. In this paper we provide experimental results related to developing a content-based group recommender system. To this end we make two important contributions. (1) Implementation of a group recommender system based on as proposed recently in vineet using MovieLens dataset which is a relatively huge data-set (100,000 ratings from 943 users on 1682 movies) as compared to the data-set size of 150 used in vineet (2) We use seven variants of decision-tree measures and built an empirical comparison table to check for precision rate in group recommendations based on different social-choice theory strategies.


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Paper Citation

in Harvard Style

Padmanabhan V., Kiran P. and Sattar A. (2013). Group Recommender Systems - Some Experimental Results . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 370-376. DOI: 10.5220/0004331003700376

in Bibtex Style

author={Vineet Padmanabhan and Prabhu Kiran and Abdul Sattar},
title={Group Recommender Systems - Some Experimental Results},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Group Recommender Systems - Some Experimental Results
SN - 978-989-8565-39-6
AU - Padmanabhan V.
AU - Kiran P.
AU - Sattar A.
PY - 2013
SP - 370
EP - 376
DO - 10.5220/0004331003700376