2020/12/10 by Liang Han, Han, Liang, Zhaozheng Yin +7 · 1 citation
Business, Management and Accounting · Decision Sciences · #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2012.06008
openalex publication_date 2020/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.