TY - GEN
T1 - Transfer Learning in Counterfeit Goods Detection
AU - Mamonov, Stanislav
AU - Pendleton, Olga
AU - Shortino, Sydney
AU - Herrera, Luis
N1 - Publisher Copyright:
© 2023 29th Annual Americas Conference on Information Systems, AMCIS 2023. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Counterfeit goods, defined as products that are made to look like genuine or original items but are actually fake or imitation versions, constitute a global problem. OECD estimates that global trade in counterfeit goods has reached $509 billion in 2016 and it continues to grow (European Union Intellectual Property Office, 2021). E-commerce wherein businesses and consumers buy and sell goods via the internet has reached $1 trillion in the United States in 2022 (United States Census, 2023). E-commerce platforms, e.g. Amazon.com, often offer access to third-party sellers. Estimates suggest that there are over 2 million third-party sellers on Amazon.com and third-party sellers now account for 60% of all unit sales on Amazon.com (Amazon Inc., 2023). Third-party sellers often engage in selling counterfeit goods in e-commerce platforms. Despite Amazon’s efforts, the sale of counterfeit goods remains a challenge for the platform (Myers, 2023). Consumer reviews offer a potential source of information regarding the sale of counterfeit products in ecommerce platforms (Wimmer & Yoon, 2017). We assess the predictive value of consumer reviews towards identification of counterfeit goods and we also examine the potential for transfer learning (Day & Khoshgoftaar, 2017), i.e. the use of classification models developed to identify instances of consumer reports of counterfeit goods in the context of one product to identify reviews signaling counterfeit goods in other product categories.
AB - Counterfeit goods, defined as products that are made to look like genuine or original items but are actually fake or imitation versions, constitute a global problem. OECD estimates that global trade in counterfeit goods has reached $509 billion in 2016 and it continues to grow (European Union Intellectual Property Office, 2021). E-commerce wherein businesses and consumers buy and sell goods via the internet has reached $1 trillion in the United States in 2022 (United States Census, 2023). E-commerce platforms, e.g. Amazon.com, often offer access to third-party sellers. Estimates suggest that there are over 2 million third-party sellers on Amazon.com and third-party sellers now account for 60% of all unit sales on Amazon.com (Amazon Inc., 2023). Third-party sellers often engage in selling counterfeit goods in e-commerce platforms. Despite Amazon’s efforts, the sale of counterfeit goods remains a challenge for the platform (Myers, 2023). Consumer reviews offer a potential source of information regarding the sale of counterfeit products in ecommerce platforms (Wimmer & Yoon, 2017). We assess the predictive value of consumer reviews towards identification of counterfeit goods and we also examine the potential for transfer learning (Day & Khoshgoftaar, 2017), i.e. the use of classification models developed to identify instances of consumer reports of counterfeit goods in the context of one product to identify reviews signaling counterfeit goods in other product categories.
UR - http://www.scopus.com/inward/record.url?scp=85192892315&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85192892315
T3 - 29th Annual Americas Conference on Information Systems, AMCIS 2023
BT - 29th Annual Americas Conference on Information Systems, AMCIS 2023
PB - Association for Information Systems
T2 - 29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023
Y2 - 10 August 2023 through 12 August 2023
ER -