TY - JOUR
T1 - A new recommendation approach based on probabilistic soft clustering methods: A scientific documentation case study
AU - Hurtado, Remigio
AU - Bobadilla, Jesus
AU - Bojorque, Rodolfo
AU - Ortega, Fernando
AU - Li, Xin
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Recommender system (RS) clustering is an important issue, both for the improvement of the collaborative filtering (CF) accuracy and to obtain analytical information from their high sparse datasets. RS items and users usually share features belonging to different clusters, e.g., a musical-comedy movie. Soft clustering, therefore, is the CF clustering's most natural approach. In this paper, we propose a new prediction approach for probabilistic soft clustering methods. In addition, we put to test a not traditional scientific documentation CF dataset: SD4AI, and we compare results with the MovieLens baseline. Not traditional CF datasets have challenging features, such as not regular rating frequency distributions, broad range of rating values, and a particularly high sparsity. The results show the suitability of using soft-clustering approaches, where their probabilistic overlapping parameters find optimum values when balanced hard/soft clustering is used. This paper opens some promising lines of research, such as RSs' use in the scientific documentation field, the Internet of Things-based datasets processing, and design of new model-based soft clustering methods.
AB - Recommender system (RS) clustering is an important issue, both for the improvement of the collaborative filtering (CF) accuracy and to obtain analytical information from their high sparse datasets. RS items and users usually share features belonging to different clusters, e.g., a musical-comedy movie. Soft clustering, therefore, is the CF clustering's most natural approach. In this paper, we propose a new prediction approach for probabilistic soft clustering methods. In addition, we put to test a not traditional scientific documentation CF dataset: SD4AI, and we compare results with the MovieLens baseline. Not traditional CF datasets have challenging features, such as not regular rating frequency distributions, broad range of rating values, and a particularly high sparsity. The results show the suitability of using soft-clustering approaches, where their probabilistic overlapping parameters find optimum values when balanced hard/soft clustering is used. This paper opens some promising lines of research, such as RSs' use in the scientific documentation field, the Internet of Things-based datasets processing, and design of new model-based soft clustering methods.
KW - Soft clustering
KW - collaborative filtering
KW - recommender systems
KW - scientific documentation
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85060687512&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85060687512&origin=inward
UR - http://www.mendeley.com/research/new-recommendation-approach-based-probabilistic-soft-clustering-methods-scientific-documentation-cas
U2 - 10.1109/ACCESS.2018.2890079
DO - 10.1109/ACCESS.2018.2890079
M3 - Article
VL - 7
SP - 7522
EP - 7534
JO - IEEE Access
JF - IEEE Access
M1 - 8594540
ER -