Detecting biased user-product ratings for online products using opinion mining
Collaborative filtering recommender system (CFRS) plays a vital role in today’s e-commerce industry. CFRSs collect ratings from the users and predict recommendations for the targeted product. Conventionally, CFRS uses the user-product ratings to make recommendations. Often these user-product ratings...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-01-01
|
| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2022-9030 |
| _version_ | 1797848636712812544 |
|---|---|
| author | Chopra Akanksha Bansal Dixit Veer Sain |
| author_facet | Chopra Akanksha Bansal Dixit Veer Sain |
| author_sort | Chopra Akanksha Bansal |
| collection | DOAJ |
| description | Collaborative filtering recommender system (CFRS) plays a vital role in today’s e-commerce industry. CFRSs collect ratings from the users and predict recommendations for the targeted product. Conventionally, CFRS uses the user-product ratings to make recommendations. Often these user-product ratings are biased. The higher ratings are called push ratings (PRs) and the lower ratings are called nuke ratings (NRs). PRs and NRs are injected by factitious users with an intention either to aggravate or degrade the recommendations of a product. Hence, it is necessary to investigate PRs or NRs and discard them. In this work, opinion mining approach is applied on textual reviews that are given by users for a product to detect the PRs and NRs. The work also examines the effect of PRs and NRs on the performance of CFRS by evaluating various measures such as precision, recall, F-measure and accuracy. |
| first_indexed | 2024-04-09T18:31:45Z |
| format | Article |
| id | doaj.art-341e3f7ebdf94c858d48099cb18383e1 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-09T18:31:45Z |
| publishDate | 2023-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-341e3f7ebdf94c858d48099cb18383e12023-04-11T17:07:17ZengDe GruyterJournal of Intelligent Systems2191-026X2023-01-013213142510.1515/jisys-2022-9030Detecting biased user-product ratings for online products using opinion miningChopra Akanksha Bansal0Dixit Veer Sain1Shyama Prasad Mukherji College for Women, Punjabi Bagh, New Delhi 110026, IndiaAtma Ram Sanatan Dharma College, University of Delhi, Dhaula Kuan, New Delhi 110021, IndiaCollaborative filtering recommender system (CFRS) plays a vital role in today’s e-commerce industry. CFRSs collect ratings from the users and predict recommendations for the targeted product. Conventionally, CFRS uses the user-product ratings to make recommendations. Often these user-product ratings are biased. The higher ratings are called push ratings (PRs) and the lower ratings are called nuke ratings (NRs). PRs and NRs are injected by factitious users with an intention either to aggravate or degrade the recommendations of a product. Hence, it is necessary to investigate PRs or NRs and discard them. In this work, opinion mining approach is applied on textual reviews that are given by users for a product to detect the PRs and NRs. The work also examines the effect of PRs and NRs on the performance of CFRS by evaluating various measures such as precision, recall, F-measure and accuracy.https://doi.org/10.1515/jisys-2022-9030collaborative filtering recommender systempush ratingsnuke ratingsopinion mining |
| spellingShingle | Chopra Akanksha Bansal Dixit Veer Sain Detecting biased user-product ratings for online products using opinion mining Journal of Intelligent Systems collaborative filtering recommender system push ratings nuke ratings opinion mining |
| title | Detecting biased user-product ratings for online products using opinion mining |
| title_full | Detecting biased user-product ratings for online products using opinion mining |
| title_fullStr | Detecting biased user-product ratings for online products using opinion mining |
| title_full_unstemmed | Detecting biased user-product ratings for online products using opinion mining |
| title_short | Detecting biased user-product ratings for online products using opinion mining |
| title_sort | detecting biased user product ratings for online products using opinion mining |
| topic | collaborative filtering recommender system push ratings nuke ratings opinion mining |
| url | https://doi.org/10.1515/jisys-2022-9030 |
| work_keys_str_mv | AT chopraakankshabansal detectingbiaseduserproductratingsforonlineproductsusingopinionmining AT dixitveersain detectingbiaseduserproductratingsforonlineproductsusingopinionmining |