Authors:
Amir Hossein Nabizadeh
;
Alípio Mário Jorge
and
José Paulo Leal
Affiliation:
INESC TEC and Universidade do Porto, Portugal
Keyword(s):
Recommender System, Collaborative Filtering, Content Based Filtering, Persuasive Recommender System, Learning Design, Matrix Factorization, Long Term Recommender System.
Related
Ontology
Subjects/Areas/Topics:
Enterprise Information Systems
;
Recommendation Systems
;
Software Agents and Internet Computing
Abstract:
The main goal of recommender systems is to assist users in finding items of their interest in very large collections.
The use of good automatic recommendation promotes customer loyalty and user satisfaction because it
helps users to attain their goals. Current methods focus on the immediate value of recommendations and are
evaluated as such. This is insufficient for long term goals, either defined by users or by platform managers.
This is of interest in recommending learning resources to learn a target concept, and also when a company is
organizing a campaign to lead users to buy certain products or moving to a different customer segment. Therefore,
we believe that it would be useful to develop recommendation algorithms that promote the goals of users
and platform managers (e.g. e-shop manager, e-learning tutor, ministry of culture promotor). Accordingly, we
must define appropriate evaluation methodologies and demonstrate the concept on practical cases.