  Recommender systems have achieved great success in modeling user's
preferences on items and predicting the next item the user would consume.
Recently, there have been many efforts to utilize time information of users'
interactions with items to capture inherent temporal patterns of user behaviors
and offer timely recommendations at a given time. Existing studies regard the
time information as a single type of feature and focus on how to associate it
with user preferences on items. However, we argue they are insufficient for
fully learning the time information because the temporal patterns of user
preference are usually heterogeneous. A user's preference for a particular item
may 1) increase periodically or 2) evolve over time under the influence of
significant recent events, and each of these two kinds of temporal pattern
appears with some unique characteristics. In this paper, we first define the
unique characteristics of the two kinds of temporal pattern of user preference
that should be considered in time-aware recommender systems. Then we propose a
novel recommender system for timely recommendations, called TimelyRec, which
jointly learns the heterogeneous temporal patterns of user preference
considering all of the defined characteristics. In TimelyRec, a cascade of two
encoders captures the temporal patterns of user preference using a proposed
attention module for each encoder. Moreover, we introduce an evaluation
scenario that evaluates the performance on predicting an interesting item and
when to recommend the item simultaneously in top-K recommendation (i.e.,
item-timing recommendation). Our extensive experiments on a scenario for item
recommendation and the proposed scenario for item-timing recommendation on
real-world datasets demonstrate the superiority of TimelyRec and the proposed
attention modules.
