One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level
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In this proposal we present the idea of a "macro recommender system", and "micro recommender system". Both systems can be considered as a recommender system for recommendation algorithms. A macro recommender system recommends the best performing recommendation algorithm to an organization that wants to build a recommender system. This way, an organization does not need to test many algorithms over long periods to find the best one for their particular platform. A micro recommender system recommends the best performing recommendation algorithm for each individual recommendation request. This proposal is based on the premise that there is no single-best algorithm for all users, items, and contexts. For instance, a micro recommender system might recommend one algorithm when recommendations for an elderly male user in the evening should be created. When recommendations for a young female user in the morning should be given, the micro recommender system might recommend a different algorithm.
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