Authors:
Esteban García-Cuesta
1
;
Daniel Gómez-Vergel
1
;
Luis Gracias Expósito
1
and
María Vela-Pérez
2
Affiliations:
1
Universidad Europea de Madrid, Spain
;
2
Universidad Complutense de Madrid, Spain
Keyword(s):
User Opinion, Recommendation Systems, User Modeling, Prediction, Hyper-personalization.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Data Engineering
;
Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Matrix Factorization
;
Natural Language Processing
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Software Engineering
;
Symbolic Systems
;
Theory and Methods
;
Web Applications
Abstract:
The rapid proliferation of social network services (SNS) gives people the opportunity to express their thoughts,
opinions, and tastes on a wide variety of subjects such as movies or commercial items. Most item shopping
websites currently provide SNS systems to collect users’ opinions, including rating and text reviews. In
this context, user modeling and hyper-personalization of contents reduce information overload and improve
both the efficiency of the marketing process and the user’s overall satisfaction. As is well known, users’
behavior is usually subject to sparsity and their preferences remain hidden in a latent subspace. A majority
of recommendation systems focus on ranking the items by describing this subspace appropriately but neglect
to properly justify why they should be recommended based on the user’s opinion. In this paper, we intend to
extract the intrinsic opinion subspace from users’ text reviews –by means of collaborative filtering techniques–
in order to capture thei
r tastes and predict their future opinions on items not yet reviewed. We will show how users’ reviews can be predicted by using a set of words related to their opinions.
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