  User-generated item lists are a popular feature of many different platforms.
Examples include lists of books on Goodreads, playlists on Spotify and YouTube,
collections of images on Pinterest, and lists of answers on question-answer
sites like Zhihu. Recommending item lists is critical for increasing user
engagement and connecting users to new items, but many approaches are designed
for the item-based recommendation, without careful consideration of the complex
relationships between items and lists. Hence, in this paper, we propose a novel
user-generated list recommendation model called AttList. Two unique features of
AttList are careful modeling of (i) hierarchical user preference, which
aggregates items to characterize the list that they belong to, and then
aggregates these lists to estimate the user preference, naturally fitting into
the hierarchical structure of item lists; and (ii) item and list consistency,
through a novel self-attentive aggregation layer designed for capturing the
consistency of neighboring items and lists to better model user preference.
Through experiments over three real-world datasets reflecting different kinds
of user-generated item lists, we find that AttList results in significant
improvements in NDCG, Precision@k, and Recall@k versus a suite of
state-of-the-art baselines. Furthermore, all code and data are available at
https://github.com/heyunh2015/AttList.
