A proposed configurable approach for recommendation systems via data mining techniques

ABSTRACT

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.

KEYWORDS:

• Recommendation systems
• clustering
• features’ selection
• data mining
• sampling

Disclosure statement

No potential conflict of interest was reported by the authors.