Abstract
Deep learning techniques have been widely applied in modern recommendation systems, leading
to flexible and effective ways of user representation. Conventionally, user representations are generated purely in the offline stage. Without referencing to the specific candidate item for recommendation, it is difficult to fully capture user preference from the perspective of interest. More recent
algorithms tend to generate user representation at
runtime, where user’s historical behaviors are attentively summarized w.r.t. the presented candidate
item. In spite of the improved efficacy, it is too expensive for many real-world scenarios because of
the repetitive access to user’s entire history. In this
work, a novel user representation framework, Hi-Fi
Ark, is proposed. With Hi-Fi Ark, user history is
summarized into highly compact and complementary vectors in the offline stage, known as archives.
Meanwhile, user preference towards a specific candidate item can be precisely captured via the attentive aggregation of such archives. As a result, both
deployment feasibility and superior recommendation efficacy are achieved by Hi-Fi Ark. The effectiveness of Hi-Fi Ark is empirically validated
on three real-world datasets, where remarkable and
consistent improvements are made over a variety of
well-recognized baseline methods