Item-Based Collaborative Filtering and Association Rules for a Baseline Recommender in E-Commerce

Jessica Lourenco, Aparna S. Varde

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

1 Scopus citations

Abstract

In the ever-growing data-driven world today, data increases in many forms, e.g. e-commerce sites uploading new products, streaming services adding TV shows and movies, and music platforms uploading new songs. It would be highly infeasible for end users to quickly browse all this data. Hence recommender systems can benefit end users (individuals as well as companies) in efficiently finding suitable products. Rather than making end users search through a vast array of items, recommender systems can suggest suitable items to users based on popularity of the items and the respective users' buying behavior. Accordingly, in this paper we explore two techniques widespread in recommender systems, i.e. item-based collaborative filtering and association rule mining, over Amazon review data on cellphones and accessories, and build a baseline recommender system scalable to larger data. Association rule mining is explored using the Apriori algorithm to find patterns in the data from transaction history. Item-based collaborative filtering is deployed using a correlation matrix to find similar products. Both these techniques yield useful results as evident from our baseline experiments. This work constitutes an exploratory study with longtime products in e-commerce and sets the stage for mining online data on relatively new products pertinent to the Covid-19 pandemic. These include face masks, hand sanitizers, disinfectant sprays, antibacterial wipes etc. Since multiple vendors are designing such crucial products today, it is important to provide recommendations to potential buyers. An ultimate goal in our work is to build a recommender app for e-commerce based on interesting results from our findings. This work constitutes intelligent data mining scalable over big data in e-commerce. It makes broader impacts on smart cities, since this fits the smart living and smart economy characteristics.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4636-4645
Number of pages10
ISBN (Electronic)9781728162515
DOIs
StatePublished - 10 Dec 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: 10 Dec 202013 Dec 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period10/12/2013/12/20

Keywords

  • Apriori algorithm
  • baseline recommender
  • collaborative filtering
  • Covid-19
  • data mining
  • decision support system
  • e-commerce
  • exploratory research
  • scalability
  • smart cities

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