Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Show, price and negotiate: a negotiator with online value look-ahead
Author: Parvaneh, A.
Abbasnejad, E.
Wu, Q.
Shi, Q.
Van Den Hengel, A.
Citation: IEEE Transactions on Multimedia, 2022; 24:1426-1434
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2022
ISSN: 1520-9210
Statement of
Amin Parvaneh, Ehsan Abbasnejad, Qi Wu, Javen Shi, and Anton van den Hengel
Abstract: Negotiation, as a seller or buyer, is an essential and complicated aspect of online shopping. It is challenging for an intelligent agent because it requires (1) extracting and utilising information from multiple sources (e.g. photos, texts, and numerals), (2) predicting a suitable price for the products to reach the best possible agreement, (3) expressing the intention conditioned on the price in a natural language and (4) consistent pricing. Conventional dialog systems do not address these problems well. For example, we believe that the price should be the driving factor for the negotiation and understood by the agent. But conventionally, the price was simply treated as a word token i.e. being part of a sentence and sharing the same word embedding space with other words. To that end, we propose our Visual Negotiator that comprises of an end-to-end deep learning model that anticipates an initial agreement price and updates it while generating compelling supporting dialog. For (1), our visual negotiator utilises attention mechanism to extract relevant information from the images and textual description, and feeds the price (and later refined price) as separate important input to several stages of the system, instead of simply being part of a sentence; For (2), we use the attention to learn a price embedding to estimate an initial value; Subsequently, for (3) we generate the supporting dialog in an encoder-decoder fashion that utilises the price embedding. Further, we use a hierarchical recurrent model that learns to refine the price at one level while generating the supporting dialog in another; For (4), this hierarchical model provides consistent pricing. Empirically, we show that our model significantly improves negotiation on the CraigslistBargain dataset, in terms of the agreement price, price consistency, and the language quality.
Keywords: Goal-oriented dialogue system; modular deep neural networks; online value estimation; reinforcement learning; visual negotiation
Description: Date of publication March 10, 2021; date of current versionMarch 29, 2022.
Rights: © 2021 IEEE.
DOI: 10.1109/TMM.2021.3065169
Grant ID:
Published version:
Appears in Collections:Aurora harvest 4
Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.