AN APPROACH FOR IMPLEMENTING A MULTIDIMENSIONAL RECOMMENDATION SYSTEM FOR ONLINE RETAIL SERVICES OR PORTALS
Keywords:Data mining, Multidimensional recommendation system, Performance Analysis, PID algorithm, Product Equivalence Value, Self-Tuning
A newly proposed algorithmic approach that can be deployed in the implementation of recommendation system which can be used at the client as well as server end. PID is a closed loop, self-tuning algorithm which is predominantly implemented in mechatronics instruments where manual supervision is not feasible and it functions for corrections of errors quantifiable in physical measures. In this approach, we propose a similar outlook to an algorithm for implementation of recommendation system. There exist no physical errors in computer based systems as such but we can abstractly map this error to irrelevancy. If we were to map errors in physical dimensions to irrelevancy in computer systems it can be approximately stated to be higher the error, higher the irrelevancy. Also, the availability of multiple factor assessment in PID algorithms can be used to add multidimensional approach in implementation. As proposed and analysed the implementation is expected to provide with faster as well as large data compliant analytics option.
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