Price Suggestion for Online Second-hand Items with Texts and Images

TL;DR

This paper introduces a multi-modal system that predicts prices for second-hand items using images, text, and statistical features, aiming to assist sellers in setting reasonable prices and improving online transactions.

Contribution

It proposes a novel multi-modal price suggestion system with a binary classification and regression approach, including a customized loss function and evaluation metrics, validated on real-world data.

Findings

Effective price prediction demonstrated on large dataset

Improved seller gain and transaction facilitation

Robust multi-modal feature integration

Abstract

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.