A Learning Framework with Disposable Auxiliary Networks for Early Prediction of Product Success

Chih-Ting Yeh, Zhe-Li Lin, Sheng-Chieh Lin, Jing-Kai Lou, Ming-Feng Tsai, Chuan-Ju Wang
{"title":"A Learning Framework with Disposable Auxiliary Networks for Early Prediction of Product Success","authors":"Chih-Ting Yeh, Zhe-Li Lin, Sheng-Chieh Lin, Jing-Kai Lou, Ming-Feng Tsai, Chuan-Ju Wang","doi":"10.1145/3486622.3493923","DOIUrl":null,"url":null,"abstract":"Consider the scenario in which an investor seeks to identify potential products before they are unveiled to the public. For such a scenario, the investor poses questions such as “What characteristic better represents a product?” or “What features make a product popular?” Unlike traditional recommendation problems, in this case, there is no user feedback for the upcoming products, which greatly complicates prediction. To address this challenging yet common scenario, we present an unconventional multi-task learning framework that trains the prediction model on information for mature products that have user feedback, and then uses this model to predict the success of upcoming products for which there is no user feedback. To achieve this goal, we train a main task network to extract product features from their descriptions while at the same time training a disposable auxiliary network to learn domain-specific words and popular trends from user reviews.This disposable auxiliary network is beneficial during the training of the main task network but is unused at the inference stage. Empirical results on two real-world datasets in different languages demonstrate that the proposed framework not only improves the overall rating prediction for products but also identifies the top successful products without any user reviews.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"34 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3486622.3493923","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Consider the scenario in which an investor seeks to identify potential products before they are unveiled to the public. For such a scenario, the investor poses questions such as “What characteristic better represents a product?” or “What features make a product popular?” Unlike traditional recommendation problems, in this case, there is no user feedback for the upcoming products, which greatly complicates prediction. To address this challenging yet common scenario, we present an unconventional multi-task learning framework that trains the prediction model on information for mature products that have user feedback, and then uses this model to predict the success of upcoming products for which there is no user feedback. To achieve this goal, we train a main task network to extract product features from their descriptions while at the same time training a disposable auxiliary network to learn domain-specific words and popular trends from user reviews.This disposable auxiliary network is beneficial during the training of the main task network but is unused at the inference stage. Empirical results on two real-world datasets in different languages demonstrate that the proposed framework not only improves the overall rating prediction for products but also identifies the top successful products without any user reviews.
基于一次性辅助网络的产品成功早期预测学习框架
考虑这样一个场景:一个投资者试图在潜在产品向公众推出之前识别它们。对于这种情况,投资者会提出这样的问题:“什么样的特征更能代表一种产品?”或“什么特性使产品受欢迎?”与传统的推荐问题不同,在这种情况下,没有用户对即将推出的产品的反馈,这大大复杂化了预测。为了解决这个具有挑战性但又常见的场景,我们提出了一个非常规的多任务学习框架,该框架根据有用户反馈的成熟产品的信息训练预测模型,然后使用该模型预测没有用户反馈的即将推出的产品的成功。为了实现这一目标,我们训练了一个主任务网络从产品描述中提取产品特征,同时训练了一个一次性辅助网络从用户评论中学习领域特定词汇和流行趋势。这种一次性辅助网络在主任务网络的训练过程中是有益的,但在推理阶段是无用的。在两个不同语言的真实数据集上的实证结果表明,所提出的框架不仅提高了产品的整体评级预测,而且在没有任何用户评论的情况下识别出最成功的产品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信