Personalized recommendation against crowd's popular selection
This work addresses the issue of low personalization in recommendations for users in data-rich environments, though it appears incremental as it builds on existing network-based approaches.
The authors tackled the problem of personalized recommendation by addressing the overemphasis on popular items, proposing an Anti-popularity index (AP) method that enhances personality, accuracy, and diversity compared to baselines with low computational complexity.
The problem of personalized recommendation in an ocean of data attracts more and more attention recently. Most traditional researches ignore the popularity of the recommended object, which resulting in low personality and accuracy. In this Letter, we proposed a personalized recommendation method based on weighted object network, punishing the recommended object that is the crowd's popular selection, namely, Anti-popularity index(AP), which can give enhanced personality, accuracy and diversity in contrast to mainstream baselines with a low computational complexity.