  Product bundling, offering a combination of items to customers, is one of the
marketing strategies commonly used in online e-commerce and offline retailers.
A high-quality bundle generalizes frequent items of interest, and diversity
across bundles boosts the user-experience and eventually increases transaction
volume. In this paper, we formalize the personalized bundle list recommendation
as a structured prediction problem and propose a bundle generation network
(BGN), which decomposes the problem into quality/diversity parts by the
determinantal point processes (DPPs). BGN uses a typical encoder-decoder
framework with a proposed feature-aware softmax to alleviate the inadequate
representation of traditional softmax, and integrates the masked beam search
and DPP selection to produce high-quality and diversified bundle list with an
appropriate bundle size. We conduct extensive experiments on three public
datasets and one industrial dataset, including two generated from co-purchase
records and the other two extracted from real-world online bundle services. BGN
significantly outperforms the state-of-the-art methods in terms of quality,
diversity and response time over all datasets. In particular, BGN improves the
precision of the best competitors by 16\% on average while maintaining the
highest diversity on four datasets, and yields a 3.85x improvement of response
time over the best competitors in the bundle list recommendation problem.
