Multi-Source Learning for Sales Prediction

Kuen Han Tsai, Yau Shian Wang, Hsuan Yu Kuo, Jui Yi Tsai, Ching Chih Chang, Hui Ju Hung, Hong-Han Shuai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the convenience and popularity of Internet, the sales on e-commerce platforms have grown significantly. This exponential growth generates massive chunks of data, which provides the opportunity to utilize the historical data for predicting the customers' behaviors and can thus offer better services. One of the major tasks is to correctly estimate the coming sales of products since the e-retailers can reserve the products in a smart way. However, even with massive data, it is still challenging to estimate the sale amount of each product due to 1) the complicated language structures, 2) difficulties in integrating the features, and 3) missing values. To address these issues, we propose a framework for predicting the sales, which contains two phases: 1) feature extraction from product attributes and reviews, and 2) tensor decomposition for multi-source learning. The experimental results show that our framework outperforms baselines by 73%.

Original languageEnglish
Title of host publicationProceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages148-153
Number of pages6
ISBN (Electronic)9781538642030
DOIs
StatePublished - 9 May 2018
Event2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 - Taipei, Taiwan
Duration: 1 Dec 20173 Dec 2017

Publication series

NameProceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017

Conference

Conference2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
CountryTaiwan
CityTaipei
Period1/12/173/12/17

Keywords

  • Machine learning
  • natural language processing
  • tensor decomposition

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