  User-generated item lists are popular on many platforms. Examples include
video-based playlists on YouTube, image-based lists (or"boards") on Pinterest,
book-based lists on Goodreads, and answer-based lists on question-answer forums
like Zhihu. As users create these lists, a common challenge is in identifying
what items to curate next. Some lists are organized around particular genres or
topics, while others are seemingly incoherent, reflecting individual
preferences for what items belong together. Furthermore, this heterogeneity in
item consistency may vary from platform to platform, and from sub-community to
sub-community. Hence, this paper proposes a generalizable approach for
user-generated item list continuation. Complementary to methods that exploit
specific content patterns (e.g., as in song-based playlists that rely on audio
features), the proposed approach models the consistency of item lists based on
human curation patterns, and so can be deployed across a wide range of varying
item types (e.g., videos, images, books). A key contribution is in
intelligently combining two preference models via a novel consistency-aware
gating network - a general user preference model that captures a user's overall
interests, and a current preference priority model that captures a user's
current (as of the most recent item) interests. In this way, the proposed
consistency-aware recommender can dynamically adapt as user preferences evolve.
Evaluation over four datasets(of songs, books, and answers) confirms these
observations and demonstrates the effectiveness of the proposed model versus
state-of-the-art alternatives. Further, all code and data are available at
https://github.com/heyunh2015/ListContinuation_WSDM2020.
