Personalized recommendation based on the personal innovator degree
Noriaki Kawamae, Hitoshi Sakano, Takeshi Yamada · 2009
This paper proposes a novel Collaborative Filtering scheme; it focuses on the dynamics and precedence of user preference to recommend items that match the latest preference of the target user. In predicting which items this user will purchase in the near future, the proposed algorithm identifies purchase history logs of users who have similar preferences and a high degree of purchase precedence (i.e., purchasing the same items earlier) relative to the target user. We call this metric the Personal Innovator Degree (PID). Experiments using real online sales data show that the proposed method outperforms existing methods.