Authors:
Ludovico Boratto
and
Salvatore Carta
Affiliation:
Università di Cagliari, Italy
Keyword(s):
Group Recommendation, Clustering, Curse of Dimensionality, Collaborative Filtering, Prediction Accuracy.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Collaborative Computing
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
;
Web Information Systems and Technologies
Abstract:
A characteristic of most datasets is that the number of data points is much lower than the number of dimensions (e.g., the number of movies rated by a user is much lower than the number of movies in a dataset). Dealing with high-dimensional and sparse data leads to problems in the classification process, known as curse of dimensionality. Previous researches presented approaches that produce group recommendations by clustering users in contexts where groups are not available. In the literature it is widely-known that clustering is one of the classification forms affected by the curse of dimensionality. In this paper we propose an approach to remove sparsity from a dataset before clustering users in group recommendation. This is done by using a Collaborative Filtering approach that predicts the missing data points. In such a way, it is possible to overcome the curse of dimensionality and produce better clusterings. Experimental results show that, by removing sparsity, the accuracy of t
he group recommendations strongly increases with respect to a system that works on sparse data.
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