TY - GEN
T1 - Achieving better recommendations with overclassification
T2 - 20th International Conference on System Theory, Control and Computing, ICSTCC 2016
AU - George, Onofrei
AU - Alexandru, Archip
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/16
Y1 - 2016/12/16
N2 - With the growth of the Internet, recommendations have become a fact of life and there is a big competition between companies to get as much market share as possible. This is achievable not only through fast to market solutions but also by constantly providing high quality services. Considering the speed of current technological advancements, it is impossible to conceive a world without some form of prediction. Traditionally, recommendations have been computed using the collaborative filtering method, but the growth of available information has pushed research into other directions such as content based, demographic based, utility-based, knowledge-based and hybrids. This paper provides an introduction to collaborative filtering and content based methods, describing the underlying concepts, and discusses possible improvements using overclassification. Individual results are presented and then compared with the overclassification approach.
AB - With the growth of the Internet, recommendations have become a fact of life and there is a big competition between companies to get as much market share as possible. This is achievable not only through fast to market solutions but also by constantly providing high quality services. Considering the speed of current technological advancements, it is impossible to conceive a world without some form of prediction. Traditionally, recommendations have been computed using the collaborative filtering method, but the growth of available information has pushed research into other directions such as content based, demographic based, utility-based, knowledge-based and hybrids. This paper provides an introduction to collaborative filtering and content based methods, describing the underlying concepts, and discusses possible improvements using overclassification. Individual results are presented and then compared with the overclassification approach.
KW - Apriori Rocchio
KW - Naive Bayes
KW - collaborative filtering
KW - content based
KW - k nearest neighbor
KW - overclassification
KW - recommendation system
UR - http://www.scopus.com/inward/record.url?scp=85010379914&partnerID=8YFLogxK
U2 - 10.1109/ICSTCC.2016.7790700
DO - 10.1109/ICSTCC.2016.7790700
M3 - Conference contribution
AN - SCOPUS:85010379914
T3 - 2016 20th International Conference on System Theory, Control and Computing, ICSTCC 2016 - Joint Conference of SINTES 20, SACCS 16, SIMSIS 20 - Proceedings
SP - 410
EP - 416
BT - 2016 20th International Conference on System Theory, Control and Computing, ICSTCC 2016 - Joint Conference of SINTES 20, SACCS 16, SIMSIS 20 - Proceedings
A2 - Petre, Emil
A2 - Brezovan, Marius
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 October 2016 through 15 October 2016
ER -