  Recommending relevant items to users is a crucial task on online communities
such as Reddit and Twitter. For recommendation system, representation learning
presents a powerful technique that learns embeddings to represent user
behaviors and capture item properties. However, learning embeddings on online
communities is a challenging task because the user interest keep evolving. This
evolution can be captured from 1) interaction between user and item, 2)
influence from other users in the community. The existing dynamic embedding
models only consider either of the factors to update user embeddings. However,
at a given time, user interest evolves due to a combination of the two factors.
To this end, we propose Influence-aware and Attention-based Co-evolutionary
Network (IACN). Essentially, IACN consists of two key components: interaction
modeling and influence modeling layer. The interaction modeling layer is
responsible for updating the embedding of a user and an item when the user
interacts with the item. The influence modeling layer captures the temporal
excitation caused by interactions of other users. To integrate the signals
obtained from the two layers, we design a novel fusion layer that effectively
combines interaction-based and influence-based embeddings to predict final user
embedding. Our model outperforms the existing state-of-the-art models from
various domains.
