(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 12, 2017 An Approach for External Preference Mapping Improvement by Denoising Consumer Rating Data Ibtihel Rebhi National Engineering School of Tunis,Tunisia MASE Unit, Engineering School of Statistics and Information Analysis, Tunisia Dhafer Malouche MASE Unit, Engineering School of Statistics and Information Analysis, Tunisia Abstract—In this study, denoising data was advocated in sensory analysis field to remove the existing noise in consumer rating data before processing to External Preference Mapping (EPM). This technique is a data visualization used to understand consumers’ sensory profiles by relating their preferences towards products to external information about sensory characteristics of the perceived products. The output is a perceptual map which visualizes the optimal products and aspects that maximize consumers’ likings. Hence, EPM is considered as a decision tool to support the development or improvement of products and respond to market requirements. In fact, the stability of the map is affected by the high variability of judgments that make consumer rating data very noisy. This may lead to a mismatch between products features and consumers’ preferences then distorted results and wrong decisions. To remove the existing noise, the use of some filtering methods is proposed. Regularized Principal Component Analysis (RPCA) and Stein’s Unbiased Risk Estimate (SURE), based respectively on hard and soft thresholding rules, were applied to consumer rating data to separate the signal from noise and maintain only useful information about the given liking scores. As a way to compare the EPM obtained from each strategy, a sampling process was conducted to randomly select samples from noisy and cleaned data, then perform their corresponding EPM. The stability of the obtained maps was evaluated using an indicator that computes and compares distances between them, both before and after denoising. The effectiveness of this methodology was evaluated by a simulation study and a potential application was shown on real dataset. Results show that recorded distances after denoising are lower than those before in almost all cases for both RPCA and SURE. However, RPCA outperforms SURE. The corresponding map is made more stable where level lines are seen smoothed and products are better located on liking zones. Hence, noise removal reduces variability in data and brings closer preferences which improves the quality of the visualized map. Keywords—Data denoising; Regularized Principal Component Analysis; Stein’s Unbiased Risk Estimate; sensory analysis; external preference mapping stability I. I NTRODUCTION In marketing research, listening to the voice of consumers has become a fundamental strategy to make good decisions about the development or improvement of products. Sensory analysis techniques are often used as a set of multivariate statistical methods to quantify and explain consumers’ sensory perceptions towards products ()i.e taste, sight, hearing, smell and touch). The method is to conduct a survey on a sample of consumers asking them to evaluate products by rating their liking. This data is known to be called hedonic data or consumer rating data. The consumers are asked to give a liking score, on a defined scale, as overall assessment of the product. The 9- point-hedonic-scale defined by David Peryam and colleagues [1] is often used: the consumers rate products according to a score ranging from 1 to 9 such that 1 indicates that the consumer extremely dislikes the product and 9 indicates that he extremely likes it. This hedonic scale was used for rating various products such as household products, personal care products, cosmetics, etc. However, it was mainly adopted by food industries to rate food products according to consumers’ tastes, which is the case study of this investigation. Many industrial companies have made the choice to seek the opinion of consumers through a score out of 10 or over 11. In their study [2], researchers show that longer scales are also good discriminators and would be even more effective than shorter ones. On the other hand, a second data known to be called sensory data is collected. Generally, a panel of trained assessors is asked to rate exactly some sensory attributes of the same set of products during different sessions of experimentation. The data is qualified as instrumental since it gives objective descriptions considered as properties measurements of the products. Sensory data are represented as a matrix crossing panelists, products, sessions and the sensory measured attributes. Generally, the average table by product is used. In case of food products, descriptive data can also be collected from a set of measures of physico-chemical components through successive analyses in chemiometrics laboratories. A statistical analysis is then performed to connect consumer data to sensory data in order to understand consumers’ ten- dencies and retrieve sensory attributes that are drivers of their liking. External Preference Mapping (EPM) [3] is one of such methods that visually assess this relationship. The output is a perceptual map that shows the optimal products maximizing consumers’ likings and their acceptability to related aspects. Hence, EPM is considered as a decision tool to support the development of a new product or to improve existing products in order to respond to market requirements and avoid product failure. The applications vary across a wide range of fields such as automobile sector to evaluate preferences towards cars’ headlights [4], the mobile sector to characterize mobile phones and watches [5], the cosmetic sector to rate some anti-aging creams [6], etc. It is mainly used in food science to evaluate the consumers’ likings towards some food products such as www.ijacsa.thesai.org 500 | Page