I-Corps: Personalized recommender systems based on semantical clustering

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The broader impact/commercial potential of this I-Corps project is to build a recommender system which assigns values to artifacts and will also provide stakeholders in unregulated markets with better information for reducing risk and improving trust. The Internet and technology have entirely changed the nature of interactions between every business sector, including the art industry, by creating a digital marketplace and introducing additional issues for markets regulation. The proposed recommender system can be used to predict value of investment, to provide an objective value of goods in legal proceedings, insurance cases, and banks as a collateral toll. This supports financial stability of markets through better decision making and improves the trust of stakeholders in their investments. It also helps to maintain control of the commercial aspects of work. For example, the acquisitions departments of museums would be served by knowing a real value for the artifacts they buy.This I-Corps project is focused on building a recommender system for assigning values to artworks using existing datasets and deep knowledge. Art markets are often misvalued which makes the classical approach to building recommender systems rather difficult due to unreliable datasets. Classifiers trained on these datasets often assign inflated prices. A crowd approach is used here which is expected to give more reliable results. Personalization for this class of problems can be achieved by assigning semantic distance. The project extends the group of classical features by adding semantic features from mining comments given by users, and, for example, from artist biographies, and images of paintings. For each cluster of semantically similar artists, a personalized recommender system for art evaluation is built. Wisdom of the crowd will be used to assign a specific value to an art piece.