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Sequential Scenario-Specific Meta Learner for Online Recommendation
Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommendation scenarios with few interactions. This paper addresses such problems usingfew-shot learning andmeta learning. Our approach is based on the insight that having a good generalization from a few …