  Sequential recommendation aims to identify and recommend the next few items
for a user that the user is most likely to purchase/review, given the user's
purchase/rating trajectories. It becomes an effective tool to help users select
favorite items from a variety of options. In this manuscript, we developed
hybrid associations models (HAM) to generate sequential recommendations using
three factors: 1) users' long-term preferences, 2) sequential, high-order and
low-order association patterns in the users' most recent purchases/ratings, and
3) synergies among those items. HAM uses simplistic pooling to represent a set
of items in the associations, and element-wise product to represent item
synergies of arbitrary orders. We compared HAM models with the most recent,
state-of-the-art methods on six public benchmark datasets in three different
experimental settings. Our experimental results demonstrate that HAM models
significantly outperform the state of the art in all the experimental settings,
with an improvement as much as 46.6%. In addition, our run-time performance
comparison in testing demonstrates that HAM models are much more efficient than
the state-of-the-art methods, and are able to achieve significant speedup as
much as 139.7 folds.
