Personalized recommendation technology provides users with more rapid and effective information acquisition channels. The existing recommendation algorithms that focus on recommendation accuracy will misguide users to a few hot commodities, thus creating many long-tail commodities. As a result, the excessive concentration of user interest is unfavorable for excavation of potential points of interest. In this paper, we proposed a reranking user-based collaborative filtering algorithm, which generated a new recommendation list via reranking of TO...
