Filter Results:
(5)
Show Results For
- All HBS Web
(74)
- Faculty Publications (5)
Show Results For
- All HBS Web
(74)
- Faculty Publications (5)
Page 1 of 5
Results
- 2019
- Working Paper
From Know-It-Alls to Learn-It-Alls: Executive Development in the Era of Self-Refining Algorithms, Collaborative Filtering and Wearable Computing
By: Mihnea Moldoveanu and Das Narayandas
We examine the future of executive education on a technological and cultural landscape that is imminent but different to the one we are accustomed to. We show how the contextualization, socialization and personalization of learning—avowed but distal goals of current... View Details
Keywords: Executive Education; Leadership Development; Technological Innovation; Customization and Personalization; Management Skills; Knowledge Acquisition; Knowledge Sharing
Moldoveanu, Mihnea, and Das Narayandas. "From Know-It-Alls to Learn-It-Alls: Executive Development in the Era of Self-Refining Algorithms, Collaborative Filtering and Wearable Computing." Harvard Business School Working Paper, No. 20-061, November 2019.
- August 2019
- Article
When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation
By: Yicheng Song, Nachiketa Sahoo and Elie Ofek
Sometimes we desire change, a break from the same or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However,... View Details
Keywords: Recommender Systems; Personalization; Recommendation Diversity; Variety Seeking; Collaborative Filtering; Consumer Utility Models; Digital Media; Clickstream Analysis; Learning-to-rank; Consumer Behavior; Media; Customization and Personalization; Strategy; Mathematical Methods
Song, Yicheng, Nachiketa Sahoo, and Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation." Management Science 65, no. 8 (August 2019): 3737–3757.
- March 2019
- Case
Wattpad
By: John Deighton and Leora Kornfeld
How to run a platform to match four million writers of stories to 75 million readers? Use data science. Make money by doing deals with television and filmmakers and book publishers. The case describes the challenges of matching readers to stories and of helping writers... View Details
Keywords: Platform Businesses; Creative Industries; Publishing; Data Science; Machine Learning; Collaborative Filtering; Women And Leadership; Managing Data Scientists; Big Data; Recommender Systems; Digital Platforms; Information Technology; Intellectual Property; Analytics and Data Science; Publishing Industry; Entertainment and Recreation Industry; Canada; United States; Philippines; Viet Nam; Turkey; Indonesia; Brazil
Deighton, John, and Leora Kornfeld. "Wattpad." Harvard Business School Case 919-413, March 2019.
- August 2018 (Revised April 2019)
- Supplement
Chateau Winery (B): Supervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on “Chateau Winery (A).” In this case, Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those... View Details
Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (B): Supervised Learning." Harvard Business School Supplement 119-024, August 2018. (Revised April 2019.)
- Research Summary
Overview
By: Feng Zhu
Professor Zhu’s research focuses on the design of platform business models and its impact on platform performance. Platforms have become central to our economy. A platform is a product or service that enables two or more customer groups to interact. For example,... View Details