Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

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Product Comments Affection Evaluation Model in Recommender System

Authors
Weiming Wang1, *
1Azman Hashim International Business School, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
*Corresponding author. Email: weiming@graduate.utm.my
Corresponding Author
Weiming Wang
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_130How to use a DOI?
Keywords
Product comments; Recommender system; Evaluation model; Neural network; Analytic hierarchy process
Abstract

Product comments is essential for the recommender platforms and assist to estimate the level of affection or sentiment expressed in customer comments for a certain product. However, existing research only concerns the single evaluation indicator volume of sales and ignore other parameters including product prices, group of buyers and the sale period. In this article, we propose a novel evaluation model to systematically estimate the products comments for recommender system and provide the certain recommend sequences by utilizing the neural networks to distinguish positive and negative comments. Subsequently, the evaluation model concerns other related evaluation parameters by utilizing the analytic hierarchy process. Proposed model can assist businesses to better understand customer opinions and preferences, and improve the customer experience. From our extensive experimental results, we can conclude that our designed mechanism can achieve approximately 82% recommend accuracy, with reasonable communication cost, which is much higher than existing evaluation model.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-198-2_130
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_130How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Weiming Wang
PY  - 2023
DA  - 2023/08/10
TI  - Product Comments Affection Evaluation Model in Recommender System
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 1258
EP  - 1264
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_130
DO  - 10.2991/978-94-6463-198-2_130
ID  - Wang2023
ER  -