PRS: PERSONNEL RECOMMENDATION SYSTEM FOR HUGE DATA ANALYSIS USING PORTER STEMMER

Personal recommendation system is one which gives better and preferential recommendation to the users to satisfy their personalized requirements such as practical applications like Webpage Preferences, Sport Videos preferences, Stock selection based on price, TV preferences, Hotel preferences, books, Mobile phones, CDs and various other products now use recommender systems. The existing Pearson Correlation Coefficient (PCC) and item-based algorithm using PCC, are called as UPCC and IPCC respectively. These systems are mainly based on only the rating services and does not consider the user personal preferences, they simply just give the result based on the ratings. As the size of data increases it will give the recommendations based on the top rated services and it will miss out most of user preferences. These are main drawbacks in the existing system which will give same results to the users based on some evaluations and rankings or rating service, they will neglect the user preferences and necessities. To address this problem we propose a new approach called, Personnel Recommendation System (PRS) for huge data analysis using Porter Stemmer to solve the above challenges. In the proposed system it provides a personalized service recommendation list to the users and recommends the most useful services to the users which will increase the accuracy and efficiency in searching better services. Particularly, a set of suggestions or keywords are provided to indicate user preferences and we used Collaborative Filtering and Porter Stemmer algorithm which gives a suitable recommendations to the users. In real, the broad experiments are conducted on the huge database which is available in real world, and outcome shows that our proposed personal recommender method extensively improves the precision and efficiency of service recommender system over the KASR method. In our approach mainly consider the user preferences so it will not miss out the any of the data, based on the ranking system and gives better preferential recommendations.