  We present a novel dynamic recommendation model that focuses on users who
have interactions in the past but turn relatively inactive recently. Making
effective recommendations to these time-sensitive cold-start users is critical
to maintain the user base of a recommender system. Due to the sparse recent
interactions, it is challenging to capture these users' current preferences
precisely. Solely relying on their historical interactions may also lead to
outdated recommendations misaligned with their recent interests. The proposed
model leverages historical and current user-item interactions and dynamically
factorizes a user's (latent) preference into time-specific and time-evolving
representations that jointly affect user behaviors. These latent factors
further interact with an optimized item embedding to achieve accurate and
timely recommendations. Experiments over real-world data help demonstrate the
effectiveness of the proposed time-sensitive cold-start recommendation model.
