Click, Click, Boom! New Algorithm Set to Boost Revenue for Online Retailers
Online shoppers hunting for the perfect product may have pages and pages of search results to scroll through. An algorithm recently developed by a team of professors incorporates customers “click and search” behavior to help online retailers make important decisions about products’ price and ranking and thus potentially boost online sales.
How Do Algorithmic Recommendations Lead Consumers to Make Online Purchases?
Many E-commerce sites such as Amazon, YouTube, and Netflix, but also online advertisers, use recommender systems. Recommender systems are algorithms that, based on data sets, recommend to users contents and products that match their preferences. In this interview, Xitong Li of HEC Paris, Associate Professor of Information Systems and a Hi! PARIS center’s research fellowship holder, reveals new research, a joint work with two German researchers, and explains how recommender systems induce consumers to buy.
HEC Paris Trains Future Professors
Our PhD Students Got Talent
This special issue aims to show the excellence and diversity of the research conducted by HEC Paris PhD candidates and alumni. You will find a selection of cutting-edge findings, answering crucial questions such as: Is AI a threat to human creativity? Should we listen to the Wall Street gurus? How to better manage one’s promotion? How much do we value our private data? What are ambiguity and risk attitudes? How bad is the mere presence of a phone? HEC Paris PhD Program, headed by finance professor Johan Hombert, supports its students throughout their thesis writing and job placement in the best universities and business schools, such as the MIT, Wharton and Harvard Business School. Most PhD alumni continue to collaborate with professors at HEC, thanks to the strong relationships they have developed during their journey.
Why Do We Share Our Personal Data?
The business model of many tech companies is based on collecting and using data about their customers. Google and Facebook generate revenues by selling targeted ads tailored to users' personal information. Amazon shows you the product they forecast you will buy. Tesla collects data on drivers' behavior and is said to plan using these data to enter into the insurance business. Google has just acquired smartwatch producer Fitbit, which collects data about users' health. The list goes on and on. So why most of us still widely share our personal data, even when we claim to be worried by the lack of online privacy?
“A $%^* Sexist Program”: Detecting and Addressing AI Bias
A major issue facing companies that use AI, algorithmic bias can perpetuate social inequalities — as well as pose legal and reputational risks to the companies in question. New research at HEC Paris offers a statistical method of tracking down and eliminating unfairness.
How Can We Force Companies To Keep Our Data Safe?
With online shopping, loyalty programs, smart devices and many other aspects of business and daily lives, companies collect vast amounts of our personal data. The risk is that they may be leaked or misused. A team of researchers designs measures that regulators and companies can undertake to preserve consumer privacy.
How Big Data Gives Insight Into Investor Uncertainty
Investor uncertainty plays a key role in economics, affecting asset prices and investment decisions. Getting a useful measure can be important to financial professionals and also government actors, to establish monetary policy. An HEC Paris researcher and two economists of the US Federal Reserve’s Board of Governors found a new way to gauge uncertainty: using data on internet clicks related to specific news.
Scientific Research: Should Negative Results Be Published?
Many call for a systematic publication of scientific negative results in order to make the production of scientific knowledge more efficient. Raphaël Lévy, Assistant Professor in the Economics and Decision Sciences Department, explains why such dissemination of knowledge may actually be beneficial, but also points to potential undesired consequences.
Legal Data Mining, Machine Learning and Visualization
Legal Data Mining Conference gathered professionals and academics from the technology, Artificial Intelligence and Law fields to discuss the future of Law. The two-day workshop focused on both the fundamental and practical issues of legal data mining. The event was organized by David Restrepo Amariles (Assistant Professor of Law at HEC Paris) and Ken Satoh (Professor at the National Institute of Informatics of Japan) in March 2019 at the Barreau de Paris.
cascad: a New Certifying Organization to Help Double-Check Scientific Results
While scientific findings need to be assessed by peers and journal referees, the confidentiality of original data often makes the process arduous. An accredited organization launched by Christophe Pérignon (HEC Paris) and colleagues with access to the original research data can now ensure reproducibility of results. This not only promises huge gains in time and effort for researchers but will also shore up trust in scientific results.