  In recommender systems (RSs), predicting the next item that a user interacts
with is critical for user retention. While the last decade has seen an
explosion of RSs aimed at identifying relevant items that match user
preferences, there is still a range of aspects that could be considered to
further improve their performance. For example, often RSs are centered around
the user, who is modeled using her recent sequence of activities. Recent
studies, however, have shown the effectiveness of modeling the mutual
interactions between users and items using separate user and item embeddings.
Building on the success of these studies, we propose a novel method called
DeePRed that addresses some of their limitations. In particular, we avoid
recursive and costly interactions between consecutive short-term embeddings by
using long-term (stationary) embeddings as a proxy. This enable us to train
DeePRed using simple mini-batches without the overhead of specialized
mini-batches proposed in previous studies. Moreover, DeePRed's effectiveness
comes from the aforementioned design and a multi-way attention mechanism that
inspects user-item compatibility. Experiments show that DeePRed outperforms the
best state-of-the-art approach by at least 14% on next item prediction task,
while gaining more than an order of magnitude speedup over the best performing
baselines. Although this study is mainly concerned with temporal interaction
networks, we also show the power and flexibility of DeePRed by adapting it to
the case of static interaction networks, substituting the short- and long-term
aspects with local and global ones.
