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The algorithm developed by Kang Zhao, an assistant professor of management sciences in the Tippie College of Business, and doctoral student Xi Wang, uses a person’s contact history to recommend partners.It’s similar to the model Netflix uses to recommend movies to users by tracking their viewing history, according to the researchers. About 28,000 of the users were men and 19,000 were women, the researchers noted, reporting that men made 80 percent of the initial contacts.The algorithm eventually notices that while a client says he likes tall women, he keeps contacting short women, and will change its recommendations to him accordingly, Zhao explained.“In our model, users with similar taste and attractiveness will have higher similarity scores than those who only share common taste or attractiveness,” Zhao says.“The model also considers the match of both taste and attractiveness when recommending dating partners.Read our guide to learn what MLA is and how to make properly formatted citations.Visual guides are also included to help you locate information needed for citations. Our comprehensive guide will teach you how to format your paper and create accurate citations.Some pro accounts may receive partial refunds - please visit support.to start a refund ticket.
To improve that rate, the researchers developed a model that combines two factors to recommend contacts: A user’s tastes, determined by the types of people he or she has contacted; and attractiveness/unattractiveness, determined by how many of those contacts are returned and how many are not.
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