  Stickers with vivid and engaging expressions are becoming increasingly
popular in online messaging apps, and some works are dedicated to automatically
select sticker response by matching the stickers image with previous
utterances. However, existing methods usually focus on measuring the matching
degree between the dialog context and sticker image, which ignores the user
preference of using stickers. Hence, in this paper, we propose to recommend an
appropriate sticker to user based on multi-turn dialog context and sticker
using history of user. Two main challenges are confronted in this task. One is
to model the sticker preference of user based on the previous sticker selection
history. Another challenge is to jointly fuse the user preference and the
matching between dialog context and candidate sticker into final prediction
making. To tackle these challenges, we propose a \emph{Preference Enhanced
Sticker Response Selector} (PESRS) model. Specifically, PESRS first employs a
convolutional based sticker image encoder and a self-attention based multi-turn
dialog encoder to obtain the representation of stickers and utterances. Next,
deep interaction network is proposed to conduct deep matching between the
sticker and each utterance. Then, we model the user preference by using the
recently selected stickers as input, and use a key-value memory network to
store the preference representation. PESRS then learns the short-term and
long-term dependency between all interaction results by a fusion network, and
dynamically fuse the user preference representation into the final sticker
selection prediction. Extensive experiments conducted on a large-scale
real-world dialog dataset show that our model achieves the state-of-the-art
performance for all commonly-used metrics. Experiments also verify the
effectiveness of each component of PESRS.
