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Review-Based Recommender System for Hedonic and Utilitarian Products in IoT Framework
Tahira, Anum Hussain, Walayat Ali, Arif
Tahira, Anum
Hussain, Walayat
Ali, Arif
Abstract
With the tremendous increase in product alternatives these days, many businesses rely heavily on recommender systems to limit the number of options they display to their customers on the front end. Many companies use the collaborative filtering algorithm and provide suggestions based on other consumers’ choices, like the active user. However, this approach faces a cold start problem and is not suitable for one-time transactions. Thus, this research aims to create a recommender system that uses online customer reviews in the IoT framework to match the attributes of a product important to the shopper. The algorithm makes recommendations by first identifying the product’s features essential to a customer. It then performs aspect-based sentiment analysis to identify those features in customer reviews and give them a sentiment score. Each customer review is weighted based on its creditably. As the impact of the recommender systems varies with the product type, an experimental study will be carried out to study the effect of the proposed algorithm differs with hedonic and utilitarian products.
Keywords
Date
2021
Type
Book chapter
Journal
Book
IoT as a Service
Volume
Issue
Page Range
221-232
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled
Notes
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
7th EAI International Conference, IoTaaS 2021, Sydney, Australia, December 13–14, 2021, Proceedings.
7th EAI International Conference, IoTaaS 2021, Sydney, Australia, December 13–14, 2021, Proceedings.