Predicting Future Interests in a Research Paper Recommender System using a Community Centric Tree of Concepts Model

Modhi Al Alshaikh, Gulden Uchyigit, Roger Evans

2017

Abstract

Our goal in this paper is to predict a user’s future interests in the research paper domain. Content-based recommender systems can recommend a set of papers that relate to a user’s current interests. However, they may not be able to predict a user’s future interests. Collaborative filtering approaches may predict a user’s future interests for movies, music or e-commerce domains. However, existing collaborative filtering approaches are not appropriate for the research paper domain, because they depend on large numbers of user ratings which are not available in the research paper domain. In this paper, we present a novel collaborative filtering method that does not depend on user ratings. Our novel method computes the similarity between users according to user profiles which are represented using the dynamic normalized tree of concepts model using the 2012 ACM Computing Classification System (CCS) ontology. Further, a community-centric tree of concepts is generated and used to make recommendations. Offline evaluations are performed using the BibSonomy dataset. Our model is compared with two baselines. The results show that our model significantly outperforms the two baselines and avoids the problem of sparsity.

Paper Citation

in Harvard Style

Al Alshaikh M., Uchyigit G. and Evans R. (2017). Predicting Future Interests in a Research Paper Recommender System using a Community Centric Tree of Concepts Model.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-271-4, pages 91-101. DOI: 10.5220/0006502900910101

in Bibtex Style

@conference{kdir17,
author={Modhi Al Alshaikh and Gulden Uchyigit and Roger Evans},
title={Predicting Future Interests in a Research Paper Recommender System using a Community Centric Tree of Concepts Model},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2017},
pages={91-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006502900910101},
isbn={978-989-758-271-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Predicting Future Interests in a Research Paper Recommender System using a Community Centric Tree of Concepts Model
SN - 978-989-758-271-4
AU - Al Alshaikh M.
AU - Uchyigit G.
AU - Evans R.
PY - 2017
SP - 91
EP - 101
DO - 10.5220/0006502900910101