Prof. Aditya Karanam is faculty in the Department of Information Systems and Analytics at the School of Computing at the National University of Singapore
27 OCTOBER, 2022: Firms strive to improve their products over time to compete effectively in the market. Typically, firms update their products by adding novel (differentiating) features or by imitating their competitors. With the ubiquity of social media, there is also the opportunity to obtain customer inputs on their preferred features for a product. In the case of mobile apps, user feedback in the form of reviews may include suggestions of novel or differentiating features or those that are already present in competing apps but not in the focal app.
Leveraging the information contained in reviews and version release notes of iOS apps, Prof. Aditya Karanam and his fellow researchers have developed a new deep learning model based on transfer learning and named entity recognition techniques to identify four types of app features: developer-initiated differentiating, developer-initiated imitative, user-suggested differentiating and user-suggested imitative. Further, they evaluated the impact of these feature categories on app demand. Their results demonstrated that only developer-initiated differentiating and user-suggested imitative features help increase app demand. They also found that the impact of user-suggested differentiating features is negative. However, this negative effect is limited to features that are contextually ”distant” from user suggestions. Contextually ”close” differentiating features suggested by users have a positive effect on the demand.
The primary contribution of their study was to investigate user reviews as a source of ideas for new features and to evaluate their performance impacts relative to those of developer-initiated features. While suggestion mining is a growing field in machine learning, their study also adds to this literature with a new deep learning model to accurately extract suggested features from user generated content. This will be of interest to academics and professionals from all walks of industry.
Date: November 25 (Friday)
Time: 11:30 am to 1:00 pm (IST)
Venue: Classroom K-13, IIM Campus
NOTES:
-
Participation through Zoom link:
Please register through link
You will be sent a Zoom link for the webinar after you register for it
-
Participation in person:
Please click here to confirm your participation mentioning the specific session
SPEAKER DETAILS
Dr. Aditya Karanam is an Assistant Professor in the Department of Information Systems and Analytics at the School of Computing in the National University of Singapore. He received his PhD from McCombs School of Business, University of Texas at Austin in 2021. His research interests are in the areas of computational social science, complex network analysis, and AI\ML based product design. His work currently focuses on integrating machine learning techniques with econometrics to derive actionable insights for mobile app developers. He has received numerous grants and awards for his research including the McCombs research excellence grant, Kauffman best student paper nomination at ICIS - 2021, and the best paper nomination at the Workshop in Information Technology and Systems in 2020. For his teaching, Aditya has been nominated for the Fred Moore Teaching Excellence Award in 2020 at UT, Austin. He also serves as a reviewer for journals such as Management Science, Information Systems Research, and Management Information Systems Quarterly. More information can be found at: https://askaranam.github.io
Prof. Aditya Karanam is faculty in the Department of Information Systems and Analytics at the School of Computing at the National University of Singapore
27 OCTOBER, 2022: Firms strive to improve their products over time to compete effectively in the market. Typically, firms update their products by adding novel (differentiating) features or by imitating their competitors. With the ubiquity of social media, there is also the opportunity to obtain customer inputs on their preferred features for a product. In the case of mobile apps, user feedback in the form of reviews may include suggestions of novel or differentiating features or those that are already present in competing apps but not in the focal app.
Leveraging the information contained in reviews and version release notes of iOS apps, Prof. Aditya Karanam and his fellow researchers have developed a new deep learning model based on transfer learning and named entity recognition techniques to identify four types of app features: developer-initiated differentiating, developer-initiated imitative, user-suggested differentiating and user-suggested imitative. Further, they evaluated the impact of these feature categories on app demand. Their results demonstrated that only developer-initiated differentiating and user-suggested imitative features help increase app demand. They also found that the impact of user-suggested differentiating features is negative. However, this negative effect is limited to features that are contextually ”distant” from user suggestions. Contextually ”close” differentiating features suggested by users have a positive effect on the demand.
The primary contribution of their study was to investigate user reviews as a source of ideas for new features and to evaluate their performance impacts relative to those of developer-initiated features. While suggestion mining is a growing field in machine learning, their study also adds to this literature with a new deep learning model to accurately extract suggested features from user generated content. This will be of interest to academics and professionals from all walks of industry.
Date: November 25 (Friday)
Time: 11:30 am to 1:00 pm (IST)
Venue: Classroom K-13, IIM Campus
NOTES:
-
Participation through Zoom link:
Please register through link
You will be sent a Zoom link for the webinar after you register for it
-
Participation in person:
Please click here to confirm your participation mentioning the specific session
SPEAKER DETAILS
Dr. Aditya Karanam is an Assistant Professor in the Department of Information Systems and Analytics at the School of Computing in the National University of Singapore. He received his PhD from McCombs School of Business, University of Texas at Austin in 2021. His research interests are in the areas of computational social science, complex network analysis, and AI\ML based product design. His work currently focuses on integrating machine learning techniques with econometrics to derive actionable insights for mobile app developers. He has received numerous grants and awards for his research including the McCombs research excellence grant, Kauffman best student paper nomination at ICIS - 2021, and the best paper nomination at the Workshop in Information Technology and Systems in 2020. For his teaching, Aditya has been nominated for the Fred Moore Teaching Excellence Award in 2020 at UT, Austin. He also serves as a reviewer for journals such as Management Science, Information Systems Research, and Management Information Systems Quarterly. More information can be found at: https://askaranam.github.io