Sequence-Aware recommendation with long-Term and short-Term attention memory networks

Abstract

Next item recommendation is an important yet challenging task in real-world applications such as E-commerce. Since people often carry out a series of online shopping activities, in order to predict what a user may purchase next, it is essential to model the user's general taste as well as the sequential correlation between purchases. Existing models combine these two factors directly without considering the dynamic changes of a user's long-Term and short-Term preferences. Meanwhile, when a purchase session contains multiple items, not all of them have the same impact on the next item to purchase. To address these limitations, we propose a model that introduces hierarchical attention to dynam..