  This paper presents an intelligent price suggestion system for online
second-hand listings based on their uploaded images and text descriptions. The
goal of price prediction is to help sellers set effective and reasonable prices
for their second-hand items with the images and text descriptions uploaded to
the online platforms. Specifically, we design a multi-modal price suggestion
system which takes as input the extracted visual and textual features along
with some statistical item features collected from the second-hand item
shopping platform to determine whether the image and text of an uploaded
second-hand item are qualified for reasonable price suggestion with a binary
classification model, and provide price suggestions for second-hand items with
qualified images and text descriptions with a regression model. To satisfy
different demands, two different constraints are added into the joint training
of the classification model and the regression model. Moreover, a customized
loss function is designed for optimizing the regression model to provide price
suggestions for second-hand items, which can not only maximize the gain of the
sellers but also facilitate the online transaction. We also derive a set of
metrics to better evaluate the proposed price suggestion system. Extensive
experiments on a large real-world dataset demonstrate the effectiveness of the
proposed multi-modal price suggestion system.
