While several approaches to evaluate Recommendation algorithms do exist, there is little research available to find best suitable approach for recommendation to a particular user. We consider using a novel game theoretic approach to find a way to evaluate the best suitable recommendation algorithm for a user over a given span of time. We formulate payoff values for the game players to find best algorithm for the user over a number of sequential, repeated games.

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