# Inferring the Importance of Product Appearance: A Step Towards the Screenless Revolution

## TL;DR

This paper develops a semi-supervised learning framework to determine the importance of product appearance in consumer decision making, aiding the transition to screenless shopping via IoT devices.

## Contribution

It introduces a novel iterative semi-supervised approach that integrates multi-view features to predict the significance of product appearance for screenless shopping.

## Key findings

- Effective in identifying items suitable for screenless shopping
- Improves prediction accuracy over baseline models
- Validated on real-world JD.com dataset

## Abstract

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.

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Source: https://tomesphere.com/paper/1905.03698