| Issue |
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|
|
|---|---|---|
| Article Number | 03030 | |
| Number of page(s) | 6 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20203203030 | |
| Published online | 29 July 2020 | |
Product Recommendation System based on User Trustworthiness & Sentiment Analysis
1 B.E., Information Technology, Vidyalankar Institute of Technology, India
2 Professor, Information Technology, Vidyalankar Institute of Technology, India
* e-mail: gunjeetks@gmail.com, amey.morje@gmail.com, dalalrohit102@gmail.com
The current online product recommendation system based on reviews has many limitations due to randomness in the review patterns. The data which is used are the reviews and ratings from the e-commerce websites. This data might contain fake reviews that make the data uncertain. Due to this, the currently existing systems produce ambiguous results on this present data. Instead of this, the new system uses only genuine reviews, considering the trustworthiness of the user and generates the results in a more significant manner. The proposed system scrapes reviews from different online websites and performs opinion mining and sentiment analysis on it. Other factors like star ratings, the buyer’s profile and previous purchases and whether the review has been given after purchasing or not are included. Based on these factors & user trustworthiness, the website from which the user should buy the product will be recommended.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.