Authors:
Modhi Al Alshaikh
;
Gulden Uchyigit
and
Roger Evans
Affiliation:
University of Brighton, United Kingdom
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 sh
ort-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.
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