  User-curated item lists, such as video-based playlists on Youtube and
book-based lists on Goodreads, have become prevalent for content sharing on
online platforms. Item list continuation is proposed to model the overall trend
of a list and predict subsequent items. Recently, Transformer-based models have
shown promise in comprehending contextual information and capturing item
relationships in a list. However, deploying them in real-time industrial
applications is challenging, mainly because the autoregressive generation
mechanism used in them is time-consuming. In this paper, we propose a novel
fast non-autoregressive sequence generation model, namely FANS, to enhance
inference efficiency and quality for item list continuation. First, we use a
non-autoregressive generation mechanism to decode next $K$ items simultaneously
instead of one by one in existing models. Then, we design a two-stage
classifier to replace the vanilla classifier used in current transformer-based
models to further reduce the decoding time. Moreover, to improve the quality of
non-autoregressive generation, we employ a curriculum learning strategy to
optimize training. Experimental results on four real-world item list
continuation datasets including Zhihu, Spotify, AotM, and Goodreads show that
our FANS model can significantly improve inference efficiency (up to 8.7x)
while achieving competitive or better generation quality for item list
continuation compared with the state-of-the-art autoregressive models. We also
validate the efficiency of FANS in an industrial setting. Our source code and
data will be available at MindSpore/models and Github.
