AN INTELLIGENT BUSINESS DECISION SUPPORT SYSTEM FOR APPAREL SELECTION BY CUSTOMERS

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1 AN INTELLIGENT BUSINESS DECISION SUPPORT SYSTEM FOR APPAREL SELECTION BY CUSTOMERS Mr.C.Perkinian 1, Dr. P. Vikkraman 2 1 Research scholar, 2 Associate Professor, School of Management Studies, Coimbatore Regional Centre, Anna University Chennai, (India) ABSTRACT Recently, apparel selection has become a difficult task due to the availability of variety of garments. Therefore, it is necessary to develop a decision support system which can provide suitable recommendations to the buyer, seller and manufacturer on the type of garments to be made and used. In this paper, we propose an intelligent business decision support system for apparel selection. The proposed system consists of two separate modules namely apparel selection system and business expert system. First module is the combination of Agent based Attribute Selection Algorithm (IAASA) and Intelligent Agent based Enhanced Multiclass Support Vector Machine (IAEMSVM) for effective apparel selection. The second module consists of Intelligent Conditional Random Field based Feature Selection algorithm (ICRFFSA) and Temporal Apriori Algorithm to provide inference on the type of garments to be made available to the customer. The experimental results obtained from this work show that the performance of the proposed intelligent business decision support system is more accurate with respect to garment recommendation. Keywords: Business Decision Support System, Feature Selection Algorithm, Conditional Random Field, Multiclass Support Vector Machine. I. INTRODUCTION Rapid growth of user generated content in the form of reviews is directly proportional to the expansion of online shopping, according to a report about sales for 2010 [1]. Current trend of business improvement is to provide sufficient facilities to the customers with low or free of cost for improving sales. Business systems are useful for making correct decisions on suitable purchase for their own interest based on experience and availability by providing more information about shopping. Due to the sharing of information about products, it is necessary to satisfy the users during purchase decisions through online / offline purchase. Because of these, the proposed system helps the customers to perform online purchase effectively by providing product information to become better buyers [2]. Moreover, it is difficult to deal with customers who do not have sufficient information about the products during their purchases [3]. In this paper, we introduced a new decision support system to simplify the business processing by providing suitable recommendations to the customers based on their interest to improve the sales. 193 P a g e

2 Feature selection [4] is playing a major role in any expert system or decision support system for fast decision making. It is the process of selecting only the necessary features from a set of available features according to the problem. The goal of obtaining optimal number of features is to improve the performance of the classification algorithm used for generating rules which are used for decision making. There are two main models that deal with feature selection namely filter methods and wrapper methods. However, filter methods are helpful to reduce the number of features. Hence, a filter method is used in this proposed system for effective feature selection. In a business information system, classification [19] is used to make effective decisions on purchase/sales. It helps in categorizing the records according to the rules. Moreover, it is used to learn a model from training and testing instances over the datasets. Classification techniques operate as either one-class classifier or multi-class classifier depending upon the application requirement. In this paper, we propose an intelligent business decision support system for apparel selection. The proposed system consists of two separate modules, one for apparel selection system and the other for business inference. The first module has been developed by applying the feature selection algorithm called IAASA [5] and the classification algorithm called IAEMSVM [5]. The second module has been developed by combining Intelligent Conditional Random Field (CRF) based Feature Selection Algorithm (ICRFFSA) [6] and the Temporal Association Rule Mining [7]. These two systems are helpful to provide recommendation on garment selection to the customers. It also helps the market and the manufacturer by providing the sales data which can be used for adjusting the amount of garments to be manufactured. The rest of this paper is organized as follows: Chapter 2 provides the related works in this area. Chapter 3 depicts the overall system architecture. Chapter 4 describes the proposed work. Chapter 5 discusses about the result and analysis. Chapter 6 gives conclusion on this work and provides some possible future works. II. LITERATURE SURVEY There are many works on apparel selection and business intelligence which are available in the literature. Among them, Teruji Sekozawa et al [8] proposed an apparel online shopping system for fashion adviser through internet. Their system analyzes the customer s choices on dresses using past data and suggests dresses suitable to the customers. Zhu Xinjuan et al [9] proposed an intelligent apparel selection model for providing personalized apparel recommendations on dresses using rules as well as the corresponding customer personal information which are used for reasoning effectively. Surendren and Bhuvaneswari [10] introduced a new Recommender System which combines content and collaborative concepts and forms association rules for decision making. The recommendation algorithm proposed by G. Karypis et al [11] uses the similarities between the various items using cosine similarity to identify the items to be recommended. Choi et al [12] developed Recommendation algorithms for helping the periodically repetitive purchasing shopping malls using association rules in the form of favorite Item set identification. Vinodhini and Chandrasekaran [13] proposed a hybrid machine learning approach for classifying the customer s opinion into positive or negative review. Kim et al [14] proposed a supervised learning approach based on Support Vector Machines to classify products according to their attributes. Dalal and Zaveri [15] provided a model using fuzzy functions for sentiment classification on user s reviews. 194 P a g e

3 III. SYSTEM ARCHITECTURE The overall architecture of the proposed system is shown in fig The proposed system architecture consists of nine major components namely market dataset, user interface, recommendation system, apparel selection system, business expert system, decision manager, rule manager, knowledge base and temporal constraint manager. Market Dataset User Interface Module Recommendation System Apparel Selection System Feature Selection (IAASA) Classification Module (IAEMSVM) Decision Manager Rule Manager Business Expert System Feature Selection (ICRFFSA) Classification Module (Temporal Apriori Algm) Temporal Constraint Manager Knowledge Base Fig. 3.1 System Architecture The user interface module collects the necessary data from the market transaction dataset. User interface module sends the data to recommendation system for recommending suitable records. The recommendation system identifies the suitable module for selecting necessary apparels with the help of the decision manager. The decision manager takes decisions based on the transactions at correct times with the help of rule manager. The rule manager extracts the necessary rules and gives the necessary inference to the decision manager for making effective decisions. IV. PROPOSED WORK This work proposes a new business decision support system for predicting new items using IAASA [5], IAEMSVM [5], ICRFFSA [6] and Temporal Association Rule Mining [7]. We have used two separate systems for predicting online shopping and offline shopping over the garment materials in various seasons such as monsoon, winter and summer. 195 P a g e

4 4.1 Feature Selection For effective feature selection, we have used two effective feature selection algorithms namelyintelligent Agent based Attribute Selection Algorithm (IAASA) [5] and Intelligent CRF based Feature Selection Algorithm (ICRFFSA)[6]. Among them, first we have selected optimal number of features from the markettransactions dataset for different seasons using ICRFFSA [6]. This algorithm chooses the optimal numbers of features for three major seasons such as summer, winter and monsoon. Next, IAASA is used for selecting suitable features for sales prediction over the past data. 4.2 Classification For effective prediction, we have used two classification algorithms namely Intelligent Agent based Multiclass Support Vector Machine (IAEMSVM) [5] and Temporal Association Rule Mining algorithm [6]. First, IAEMSVM [5] is used for predicting the apparel selection in online shopping over image database. Second, Temporal Association Rule Mining algorithm is used for predicting the products on offline shopping using market transactions data. V. RESULTS AND DISCUSSION This section discusses about the dataset which is used for evaluating the proposed system and the experimental analysis. 5.1 Apparel Data Set This section contains three datasets such as image database [16], dresses attributes [17] and dresses sales data [18]. We have used these three different datasets also for different purposes including classification and decision making. For carrying out the analysis, data were obtained from five different shops in each city and five cities namely Chennai, Bangalore, Mumbai, New Delhi and Kolkata and are used in this work. This dataset contains the item-sets sold in different dates of However, this analysis was carried out for five years starting from 2010 in the same cities and same shops. 5.2 Experimental Results In this work, a new intelligent business expert system has been proposed and implemented in MATLAB. We have considered two datasets for experiments and analysis. Among them, we have taken images from apparel Image database for analysis for finding the suitable dresses to the customers in online shopping. Each of the individual images used for identification process are matched with training images. Another data set consists of dresses sales data which is used for predictive analysis. Finally, we have made comparative analysis between questionnaires and actual sold items. Table 5.1 shows the performance comparative analysis between the manual system and intelligent system. We have used the different number of images for each experiment. Five experiments have been conducted with 500, 1000, 2000, 5000 and images respectively. 196 P a g e

5 Table 5.1 Comparative Analysis Between Manual System and Intelligent System Accuracy Analysis Ex. No. Manual System Intelligent System E E E E E From table 5.1, it can be observed that the performance of the intelligent business decision support system is better than manual system due to the use if recommendation system and rule manager for making decision on images. Here, five experiments have been conducted with different number of images for recommendation / prediction process over online purchase. Table 5.2 shows the differences between manual system and intelligent business system according to the amount of sales over the various products such as Jeans Pant, Chudidhar, Pant, Saree and T-Shirt. Table 5.2 Amount of sales in various items between manual system and intelligent system Items Name Amount of sales with Amount of sales Manual System intelligent System Jeans Pant Chudidhar Pant Saree T-Shirt From table 5.2, it can be observed that the amount of sales with intelligent system is high than the amount of sales with manual system over all the products. Table 5.3 shows the correlation between predicted sales by using intelligent systems and the actual sales in various months in the year of Table 5.3 Correlation Between Predicted and Actual Sales in Various Months Month Predicted Sales Actual Sales Jan Mar May July Sep Nov P a g e

6 From table 5.3, it can be seen that the intelligent systems predicted sales value is very closer to the actual sale in various months of Fig. 5.1 shows the comparative analysis between online business and offline business for the past five years. Fig. 5.1 Prediction Accuracy Analysis Between the Various Years From fig. 5.1, it can be observed that the sales prediction over online shopping is better than the offline sales prediction due to the use of dresses present in the image database for decision making. Offline shopping prediction analysis is made by the help of questionnaire and the past sales transactions dataset. Fig. 5.2 shows the comparative analysis between online and offline shopping over the various five major cities such as Chennai, Bangalore, Mumbai, New Delhi and Kolkata. Fig. 5.2 Prediction Accuracy Analysis BETWEEN VARIOUS CITIES From fig. 5.2, it can be observed that the prediction accuracy analysis over online shopping is better than in offline prediction analysis in various cities of India by the proposed intelligent business system. The significant of prediction accuracy analysis between online and offline is due to the use of recommendation system, temporal manager and business rules. VI. CONCLUSION AND FUTURE WORKS In this paper, an intelligent business decision support system has been proposed and implemented for apparel selection. The proposed system consists of two separate modules namely apparel selection system and business expert system. These modules use rules and temporal constraints to make effective decisions. The 198 P a g e

7 recommendations made by this system are useful not only to the customers for purchasing and also for the manufacturers to get feedback on the sales and requirements. The experimental results show that the performance of the proposed intelligent business decision support system is more accurate than the existing recommendation systems. Future works in this direction could be the introduction of soft computing techniques to handle uncertainty effectively. REFERENCES [1] Gudigantala, N, Song, J, and Jones, D. R, How well do e-commerce web sitessupport compensatory and non-compensatory decision strategies? An exploratory study, International Journal of E-Business Research, 4(4), 2008, [2] Cheng, J, Sun, A, Zeng, D, Information overload and viral marketing:countermeasures and strategies. Advances in Social Computing, Lecture Notes in Computer Science, 6007, 2010, [3] Iyengar, S. S, Wells, R.E, Schwartz, B, Doing better but feeling worse:looking for the best job undermines satisfaction, Psychological Science, 17(2), 2006, [4] S.Ganapathy, K.Kulothungan, S.Muthuraj Kumar, M.Vijayalakshmi, P.Yogesh, A.Kannan, Intelligent Feature Selection and Classification Techniques for Intrusion Detection in Networks: A Survey, EURASIP- Journal of Wireless Communications and Networking, 271, 2013, [5] Ganapathy S, Yogesh P and Kannan Arputharaj, Intelligent Agent Based Intrusion Detection System Using Enhanced Multiclass SVM, Journal of Computational Intelligence and Neuroscience, 2012, [6] S.Ganapathy, P.Vijayakumar, P.Yogesh, A.Kannan, An Intelligent CRF based Feature Selection for Effective Intrusion Detection, International Arab Journal of Information Technology, 2015, Article in online. [7] R.M. Somasundaram, K.Lakshmanan, An Intrusion Detection System for MANET using CRF Based Feature Selection and Temporal Association Rules, International Journal of Soft Computing, 8(6), 2013, [8] TerujiSekozawa, Hiroyuki Mitsuhashi, Yukio Ozawa, One-to-One Recommendation System in Apparel Online Shopping. Electronics and Communications in Japan, 94(1), 2011, [9] Zhu Xinjuan, Huang Junfang, Shi Meihong, An intelligent on-line recommendation system in B2Capparel e-commerce, 2010 International Conference on E-Business and E-Government, 2010, [10] D. Surendren, V.Bhuvaneswari, A Framework for Analysis of Purchase Dissonance in Recommender System Using Association Rule Mining, International Conference on Intelligent Computing Applications (ICICA 14), 2014, [11] G. Karypis, Evaluation of item-based top-n recommendation algorithms, Proc. of CIKM '01 Conference, 2001, [12] Y. K. Choi and S. K. Kim, Recommendation algorithms for online shopping malls with periodically purchasing users, Journal of KIISE: Software and Applications, 40(8), 2013, [13] Vinodhini, G, and Chandrasekaran, RM, Sentiment analysis and opinion mining: A survey, International Journal of advanced Research in Computer Science and Software Engineering, 2(6), 2012, P a g e

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