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Rapid development of information technology is affecting not only business organizations but also museums of art as well. Many museums are trying to adopt new technologies so that the museums can enhance their interaction and shorten the distance with customers. Among various technologies, this research focuses on collaborative filtering which is one of the widely used methods of making personalized recommendation of products to customers online based on their preferences. While the technology has been successfully used in the e-commerce field for a decade, it is hard to find actual cases of its application in museums. Hence, this paper presents an effort to investigate whether collaborative filtering can be used effectively for fine art, and if so, what factors affect the effectiveness of its application. A survey was conducted to collect data about the participants’ preferences of a range of fine art paintings. The data then were used to create preference estimations using the mechanism of collaborative filtering. The estimations were compared using different subsets of the data to identify which factors affect the accuracy of the preference estimation. It was found that collaborative filtering can successfully estimate the preference of the participants, and that the level of knowledge in art, prominence of artwork, and gender have significant effect on the accuracy of estimation. DOI: 10.5901/mjss.2015.v6n6s2p433

Testing the Application of the Personalized Recommendation Method for Artworks