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

• URL: http://arxiv.org/abs/2012.06008v1
• Date: Thu, 10 Dec 2020 22:50:42 GMT
• Title: Price Suggestion for Online Second-hand Items with Texts and Images
• Authors: Liang Han, Zhaozheng Yin, Zhurong Xia, Mingqian Tang, Rong Jin
• Abstract summary: The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items. We design a multi-modal price suggestion system which takes as input the extracted visual and textual features. We derive a set of metrics to better evaluate the proposed price suggestion system.
• Score: 39.669905951338684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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