Multidirectional Product Support System for Decision Making in Textile Industry using Collaborative Filtering Methods

Abstract

In the information technology ground people are using various tools and software for their official use and for their personal reasons. Nowadays people are worrying to choose data accessing tools and software's at the time of buying and selling the products and they are also worrying about various constraints such as cost, life time of the product, color and size of the product etc. In this paper we generated the solutions to the existing unsolved problems. Here we proposed the algorithm Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective decision at all the levels of data extraction, using the above technique and we analyzed the results at various datasets, finally the results were observed and compared with the existing methods such as PCC and VSS. The result accuracy was higher than the existing rank prediction methods. Collaborative techniques are used to filtering the noise data and give product recommendation to novice users. Collaborative filtering may involve in large data sets. Collaborative technique is used to predict user's interests on particular product or more than one product. In this Proposal Selling and buying are the two conventional activities for each seller and customer. One who sells the products is aims to gain the maximum earnings, and the customer has to get trustworthy product and it is extendable to intermediate level. These scenarios are identified and people who are used products are worried to discover the best product and in addition they suffer and face difficulty to draw the features of the item such as color, product size, availability and durability of the item. To prevail over these situations identified a problem learning multidirectional asymmetric similarity collaborative filtering via matrix factorization technique. The Collaborative filtering technique is a conventional recommender system which was used by different peoples in different circumstances, and data was extracted by this conventional method to various purposes. Still data extraction is a foremost problem in various disciplines, to handle seriously this problem and provide remedy to this issue. Solving any problem using any one the available technique is a common method, but the difficulty is to identify a best method or technique for long- term solution to the existing problem. In the exiting problem is providing the solution not up to the expected level of the customers. Customers are suffering sparsity and scalability problems. Most of the commercial recommender systems are associated with large data sets. The user-item matrix used for collaborative filtering could be tremendously large and sparse, which carries out the challenges in the performances of the recommendation. 1.1 Data Sparsity Problem One classic problem in data sparsity is the cold start problem. As collaborative filtering methods recommend items based on users' past preferences, new users must need to rate sufficient number of items to enable the system to capture their likings exactly and to provide reliable recommendations. Similarly, users when rating the new items also face the same problem. When new items are added to system, they need to be rated by large number of users before they could be recommended to users who have similar tastes with the ones rated them. The new item problem does not limit the content-based recommendation, because the recommendation of an item is based on its discrete set of descriptive qualities rather than its ratings. 1.2 Scalability Problem The Secondly Scalability is another problem in our traditional CF algorithms will face serious scalability problems. If the customer data set, no of items are high a normal CF algorithm time complexity was too large and many online version systems have to respond immediately and provide recommendations of their purchases and ratings history, which demands a higher scalability of the Collaborative filtering system.