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This document summarizes Takashi Umeda's summer seminar presentation on item-based collaborative filtering recommendation algorithms. The presentation introduced item-based CF as an improvement over traditional user-based CF algorithms. It evaluated item-based CF using movie rating data, finding it provided better prediction quality than nearest neighbor approaches, with higher online performance due to pre-computing static item similarities offline. While quality gains over nearest neighbor were small, item-based CF was shown to retain good prediction quality even when using only a subset of item similarities.
