13-03-2014, 02:14 PM
Identifying “Best” Products based on Multiple Criteria using Decision Making System
Multiple Criteria using Decision.pptx (Size: 189.95 KB / Downloads: 10)
Abstract
Most previous studies focus on how to help customers find a set of possible products from a pool of given products.
In this paper, we identify an interesting problem, finding top-k preferable products, which has not been studied before.
We study two problem instances of finding top-k preferable products.
In the first problem instance, we need to set the prices of these products such that the total profit is maximized.
We refer such products as top-k profitable products.
In the second problem instance, we want to find k products such that these k products can attract the greatest number of customers.
We refer these products as top-k popular products.
In this paper, we propose solutions to find the profitable products and popular products efficiently.
An extensive performance study using both synthetic and real data sets is reported to verify the effectiveness and efficiency of proposed algorithms.
Introduction
The algorithms can be applied on cross-domains such as for a product from different vendors.
The permutations of attributes helps to identify all possible ways for a profitable product feature.
Because many customers are interested to find the potential products ( ex:- priceline.com ).
Module9
In this modules the set ‘t’ to be converted to ‘0’ and ‘1’.
The frequent item set cross checking will be done in the Brute force approach with linear fashion.
If ‘t’ is popular then sustains in the market.
The process is iterative until we get the top-k sets, which provide profitability.
Conclusion
The 0.63 approximation, NP-Hard algorithms can be applied on large dataset.
The approaches improves the performance.