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A survey on data mining and machine learning techniques for internet voting and product/service selection


Mr. Y. Subba Reddy and Prof. P. Govindarajulu


Vol. 17  No. 9  pp. 76-84


Internet voting is the process of collection of opinions on a specific, defined issue for the purpose of collecting information about objects like people, products, and services and so on. A voting method can be used as a rating process by adding a new dimension to it in terms of the group definition of ratable objects. Social networks like Twitter, LinkedIn, Facebook, and Google+ have gained remarkable attention in recent days. People started relying more on a social network for manifold information requirements. The results in large volumes of data, and this data is very complex to analyze manually. Data mining facilitates the extraction of useful knowledge from diverse aspects of the social network, to support decision making. Voting assistance applications are basically used to advise voters in electing the right alternative. Vote recommendation systems usually exploited during elections, may be extended to the selection of suitable products and services based on user preferences, ratings, reviews, and profiles. Recommended System exploits association among users by the way of item recommendation. Mining the constructive reviews from the user comments, votes, and preferences is an interesting area of research in recent times. In this paper, a detailed study and analysis are done on the existing techniques for a recommendation of rated /voted products, services policies, and users as well. It is explored about the role of classification, clustering, and other data mining and machine learning techniques in meeting the current data analysis and information needs. The modern trends of data and the applicability of the recommendation techniques to satisfy the current information needs is pointed. The extensibility of the voting advising techniques/recommendation techniques in various contexts is discussed along with the proposals for new procedures that suit the current information needs.


Internet voting, Data mining, Clustering, Recommender system, Collaborative filtering, similarity measures.