Authors:
Rezoug Nachida
1
;
Omar Boussaid
2
and
Fahima Nader
3
Affiliations:
1
Blida University, Algeria
;
2
Lyon 2 University, France
;
3
ESI Institute, Algeria
Keyword(s):
Personalization, Recommendation, Profile, Data-warehouses, Machine-learning, Data-mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
OLAP systems facilitate analysis by providing a multidimensional data space which decision makers explore interactively by a succession of OLAP operations. However, these systems are developed for a group of decision makers or topic analysis "subject-oriented", which are presumed, have identical needs. It makes them unsuitable for a particular use. Personalization aims to better take into account the user; first this paper presents a summary of all work undertaken in this direction with a comparative study. Secondly we developed a search algorithm for class association rules between query type and user (s) to deduce the profile of a particular user or a user set in the same category. These will be extracted from the log data file of OLAP server. For this we use a variant of prediction and explanation algorithms. These profiles then form a knowledge base. This knowledge base will be used to generate automatically a rule base (ACE), for assigning weights to the attributes of data wareh
ouses by type of query and user preferences. More it will deduce the best contextual sequence of requests for eventual use in a recommended system.
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