loading
Documents

Research.Publish.Connect.

Paper

Authors: Modhi Al Alshaikh ; Gulden Uchyigit and Roger Evans

Affiliation: University of Brighton, United Kingdom

ISBN: 978-989-758-271-4

Keyword(s): Recommender System, Personalization, User Profile, Research Papers, Short-term, Long-term.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: Modelling users’ interests accurately is an important aspect of recommender systems. However, this is a challenge as users’ behaviour can vary in different domains. For example, users’ reading behaviour of research papers follows a different pattern to users’ reading of online news articles. In the case of research papers, our analysis of users’ reading behaviour shows that there are breaks in reading whereas the reading of news articles is assumed to be more continuous. In this paper, we present a novel user modelling method for representing short-term and long-term user’s interests in recommending research papers. The short-term interests are modelled using a personalised dynamic sliding window which is able to adapt its size according to the ratio of concepts per paper read by the user rather than purely time-based methods. Our long-term model is based on selecting papers that represent user’s longer term interests to build his/her profile. Existing methods for modelling user’s sho rt-term and long-term interests do not adequately take into consideration erratic reading behaviours over time that are exhibited in the research paper domain. We conducted evaluations of our short-term and long-term models and compared them with the performance of three existing methods. The evaluation results show that our models significantly outperform the existing short-term and long-term methods. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.81.220.239

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Al Alshaikh, M.; Uchyigit, G. and Evans, R. (2017). A Novel Short-term and Long-term User Modelling Technique for a Research Paper Recommender System.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 255-262. DOI: 10.5220/0006504502550262

@conference{kdir17,
author={Modhi Al Alshaikh. and Gulden Uchyigit. and Roger Evans.},
title={A Novel Short-term and Long-term User Modelling Technique for a Research Paper Recommender System},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2017},
pages={255-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006504502550262},
isbn={978-989-758-271-4},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - A Novel Short-term and Long-term User Modelling Technique for a Research Paper Recommender System
SN - 978-989-758-271-4
AU - Al Alshaikh, M.
AU - Uchyigit, G.
AU - Evans, R.
PY - 2017
SP - 255
EP - 262
DO - 10.5220/0006504502550262

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.