  Nowadays, almost all the online orders were placed through screened devices
such as mobile phones, tablets, and computers. With the rapid development of
the Internet of Things (IoT) and smart appliances, more and more screenless
smart devices, e.g., smart speaker and smart refrigerator, appear in our daily
lives. They open up new means of interaction and may provide an excellent
opportunity to reach new customers and increase sales. However, not all the
items are suitable for screenless shopping, since some items' appearance play
an important role in consumer decision making. Typical examples include
clothes, dolls, bags, and shoes. In this paper, we aim to infer the
significance of every item's appearance in consumer decision making and
identify the group of items that are suitable for screenless shopping.
Specifically, we formulate the problem as a classification task that predicts
if an item's appearance has a significant impact on people's purchase behavior.
To solve this problem, we extract features from three different views, namely
items' intrinsic properties, items' images, and users' comments, and collect a
set of necessary labels via crowdsourcing. We then propose an iterative
semi-supervised learning framework with three carefully designed loss
functions. We conduct extensive experiments on a real-world transaction dataset
collected from the online retail giant JD.com. Experimental results verify the
effectiveness of the proposed method.

Computer Vision and Pattern Recognition;Machine Learning