  In the combinatorial recommender systems, multiple items are fed to the user
at one time in the result page, where the correlations among the items have
impact on the user behavior. In this work, we model the combinatorial
recommendation as the problem of generating a sequence(ordered list) of items
from a candidate set, with the target of maximizing the expected overall
utility(e.g. total clicks) of the sequence. Toward solving this problem, we
propose the Evaluation-Generation framework. On the one hand of this framework,
an evaluation model is trained to evaluate the expected overall utility, by
fully considering the user, item information and the correlations among the
co-exposed items. On the other hand, generation policies based on heuristic
searching or reinforcement learning are devised to generate potential
high-quality sequences, from which the evaluation model select one to expose.
We propose effective model architectures and learning metrics under this
framework. We also offer series of offline tests to thoroughly investigate the
performance of the proposed framework, as supplements to the online
experiments. Our results show obvious increase in performance compared with the
previous solutions.
