  Resource constraints, e.g. limited product inventory or financial strength,
may affect consumers' choices or preferences in some recommendation tasks but
are usually ignored in previous recommendation methods. In this paper, we aim
to mine the cue of user preferences in resource-limited recommendation tasks,
for which purpose we specifically build a large used car transaction dataset
possessing resource-limitation characteristics. Accordingly, we propose an
interest-behavior multiplicative network to predict the user's future
interaction based on dynamic connections between users and items. To describe
the user-item connection dynamically, mutually-recursive recurrent neural
networks (MRRNNs) are introduced to capture interactive long-term dependencies,
and meantime effective representations of users and items are obtained. To
further take the resource limitation into consideration, a resource-limited
branch is built to specifically explore the influence of resource variation on
user preferences. Finally, mutual information is introduced to measure the
similarity between the user action and fused features to predict future
interaction, where the fused features come from both MRRNNs and
resource-limited branches. We test the performance on the built used car
transaction dataset as well as the Tmall dataset, and the experimental results
verify the effectiveness of our framework.
