Price Suggestion for Online Second-hand Items with Texts and Images
Liang Han, Zhaozheng Yin, Zhurong Xia, Mingqian Tang, Rong Jin

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…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Auction Theory and Applications · Stock Market Forecasting Methods
