A recommendation method considering users' time series contexts
Kenta Oku, Shinsuke Nakajima, Jun Miyazaki, Shunsuke Uemura, Hirokazu Kato · 2009
This paper proposes a recommendation method considering users' time series contexts which are situations that have occurred / will occur in the past/future. There are some recommendation methods that provide information suitable for users' action patterns as the recommendation methods considering them. These methods provide information referring to the other users that have a similar action pattern to that of an active user. However, since a user's action pattern changes depending on the user's contexts, the methods need to refer to the other users' action patterns related to the current user's contexts. In this paper, we propose a recommendation method considering the user's time series contexts considering that the user's action pattern changes depending on the user's contexts.