Title:
|
RECOMMENDER SYSTEM USING UNSUPERVISED
MACHINE LEARNING FOR SATISFACTION SURVEYS |
Author(s):
|
Baba Mbaye |
ISBN:
|
978-989-8533-92-0 |
Editors:
|
Ajith P. Abraham and Jörg Roth |
Year:
|
2019 |
Edition:
|
Single |
Keywords:
|
Recommender System, Unsupervised Machine Learning, Collaborative Filtering, Satisfaction Surveys |
Type:
|
Doctoral Consortium |
First Page:
|
251 |
Last Page:
|
255 |
Language:
|
English |
Cover:
|
|
Full Contents:
|
click to dowload
|
Paper Abstract:
|
Satisfaction surveys are being used more and more by companies to improve their sales force. With the development of
new technologies the piloting of these satisfaction surveys is digitized in a partial way.
Piloting these surveys often involves the expertise of a human agent in order to make a judgment on the results obtained
from satisfaction surveys. This is a tedious task for the decision-maker, as it faces a huge and heterogeneous amount of
data.
This problem may be mitigated by using a recommendation engine based on the unsupervised machine learning
algorithm. This recommendation system (RS) will be oriented towards two axes: decision-making (DM) and machine
learning (ML).
In our approach, we use RS for consistency between the user and the recommended items. ML will allow us to include in
our list of recommendations, unexpected items, items that are not derived from the algorithmic logic of the
recommendation system and to make the system partially autonomous on decision-making (to less involving the
recommendation engine). Our approach is divided into a) the recommendation process for decision-making,
b) unsupervised ML and c) partial "empowerment" for decision-making. |
|
|
|
|