Detecting biased user-product ratings for online products using opinion mining

被引:0
|
作者
Chopra, Akanksha Bansal [1 ]
Dixit, Veer Sain [2 ]
机构
[1] Shyama Prasad Mukherji Coll Women, New Delhi 110026, India
[2] Univ Delhi, Atma Ram Sanatan Dharma Coll, New Delhi 110021, India
关键词
collaborative filtering recommender system; push ratings; nuke ratings; opinion mining; RECOMMENDER SYSTEMS; SENTIMENT ANALYSIS; SIMILARITY;
D O I
10.1515/jisys-2022-9030
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Recommending Products to Customers using Opinion Mining of Online Product Reviews and Features
    Rajeev, Venkata P.
    Rekha, Smrithi, V
    2015 INTERNATIONAL CONFERENCED ON CIRCUITS, POWER AND COMPUTING TECHNOLOGIES (ICCPCT-2015), 2015,
  • [2] Product Quality Assessment using Opinion Mining in Persian Online Shopping
    HosseinzadehBendarkheili, Fatemeh
    MohammadiBaghmolaei, Rezvan
    Ahmadi, Ali
    2019 27TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE 2019), 2019, : 1917 - 1921
  • [3] Opinion Mining of Online Shopping Products Reviews Using Machine Learning
    Arra, Aashritha
    Yeboah, Jones
    Kofinti, Isaac
    2023 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE, CSCI 2023, 2023, : 270 - 276
  • [4] Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges
    Dalal, Mita K.
    Zaveri, Mukesh A.
    APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING, 2014, 2014
  • [5] AN INTEGRATED APPROACH FOR SUPERVISED LEARNING OF ONLINE USER REVIEWS USING OPINION MINING
    Shobana
    Leema, Anny
    IIOAB JOURNAL, 2016, 7 (09) : 117 - 124
  • [6] Perceptual Information for User-Product Interaction: Using Vacuum Cleaner as Example
    Chen, Li-Hao
    Lee, Chang-Franw
    INTERNATIONAL JOURNAL OF DESIGN, 2008, 2 (01): : 45 - 53
  • [7] Feature Extraction and Opinion Mining in Online Product Reviews
    Aravindan, Siddharth
    Ekbal, Asif
    2014 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY (ICIT), 2014, : 94 - 99
  • [8] Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering
    Lakshmanaprabu, S. K.
    Shankar, K.
    Gupta, Deepak
    Khanna, Ashish
    Rodrigues, Joel J. P. C.
    Pinheiro, Placido R.
    de Albuquerque, Victor Hugo C.
    COMPLEXITY, 2018,
  • [9] From Opinion Mining to Improvement Mining : Understanding Product Improvements from User Reviews
    Ramnani, Roshni R.
    Sengupta, Shubhashis
    FIRE 2021: PROCEEDINGS OF THE 13TH ANNUAL MEETING OF THE FORUM FOR INFORMATION RETRIEVAL EVALUATION, 2021, : 52 - 57
  • [10] Web Product Ranking Using Opinion Mining
    Huang, Yin-Fu
    Lin, Heng
    2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), 2013, : 184 - 190