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Novel Algorithms for Ranking and Suggesting True Popular Items

Ranking is the process of giving rank scores to the most popular item by taking user feedback. The most frequently occurring items are given the highest rank score. In practice, one may use prior information about the item popularity. For example, in the survey, the user may select the suggested item or they may also select the others. Suggestion is a list of items that are presented to the users. This is done based on the user’s feedback. The users give their preference of items through feedback and use them in the ranking of items. In this paper, our aim is to propose novel algorithms for suggesting popular items to users in a way that enables learning of the users’ true preference over items. The true preference refers to the preference over the items that would be observed from the users’ selections over items without exposure to any suggestions.