  Given a user's historical interaction sequence, online novel recommendation
suggests the next novel the user may be interested in. Online novel
recommendation is important but underexplored. In this paper, we concentrate on
recommending online novels to new users of an online novel reading platform,
whose first visits to the platform occurred in the last seven days. We have two
observations about online novel recommendation for new users. First, repeat
novel consumption of new users is a common phenomenon. Second, interactions
between users and novels are informative. To accurately predict whether a user
will reconsume a novel, it is crucial to characterize each interaction at a
fine-grained level. Based on these two observations, we propose a neural
network for online novel recommendation, called NovelNet. NovelNet can
recommend the next novel from both the user's consumed novels and new novels
simultaneously. Specifically, an interaction encoder is used to obtain accurate
interaction representation considering fine-grained attributes of interaction,
and a pointer network with a pointwise loss is incorporated into NovelNet to
recommend previously-consumed novels. Moreover, an online novel recommendation
dataset is built from a well-known online novel reading platform and is
released for public use as a benchmark. Experimental results on the dataset
demonstrate the effectiveness of NovelNet.
