Dr. Yang Wang

Many platforms employ a mix of algorithms and humans for recommending products. For example, Spotify features algorithm and editor / celebrity playlists. Amazon employs both algorithmic product recommendations and “staff picks.” Apple News features experienced editors in conjunction with algorithms when recommending products. This phenomenon begs the question, why do these big platforms, run by … Continue reading Do consumers prefer AI or human to recommend products?

The bullet point takeaways: Track crowds. Economic devastation happened with or without SIP. Link to most recent draft of working paper Like many academics, I’ve recently tossed my proverbial thinking hat into the ring to investigate the causal impact of shelter in place (SIP) policies on public health and social distancing outcomes. This is clearly … Continue reading Did Shelter in Place policies work?

First HBR now online! Much thanks to coauthors KT Manis and Alex Chaudhry for putting this concise summary of our previous JMR together. https://hbr.org/2020/05/5-principles-for-responding-to-customer-reviews The takeaways from the takeaways: Principle 1. Address a positive online review by providing a generic, short response. Principle 2. Delay responses for positive reviews. Principle 3. Respond to all negative … Continue reading 5 principles for responding to customer reviews

Wholefoods Getting groceries delivered has been a bit of a challenge recently. I had been getting deliveries from Reading Market (through Mercado which subcontracts DoorDash(?)). Their lead times are now over 2 weeks. It appears WholeFoods is taking a different approach by releasing delivery slots at random times throughout the day. However, every time I … Continue reading Sniping that WholeFoods delivery slot and other web scraping projects

If you’re anything like me, you avoid calling customer service at all costs. The anticipated pain, whether rational or not, of waiting on hold and having to deal with another human interaction is sometimes just too much of a barrier to making a phone call. I often end up using automated chat functions even though … Continue reading Designing an Effective Service Chatbot by Anticipating Customer Needs

In a new working paper on the effects of assortment size on purchase probabilities, my coauthors (Xueming Luo & Zhijie Lin) and I try to take advantage of a food delivery app’s delivery radius constraints that lead to quasi-exogenous variations in kitchen assortment sizes. In its most simplistic form, I visualized the following thought experiment: … Continue reading Approximate border identification

Companion Git repo In studying the construction of movie trailers, one of the more interesting components is figuring out how many scene changes (cuts) are included in the 2m30s of the typical trailer. While ideally one would want to match scenes from a trailer to scenes from the actual movie, we do not have access … Continue reading Identifying scene changes in videos

Companion Git repo In my current research with Alex Chaudhry, we study the relationship between trailer design and opening weekend box-office sales. One of the things we’re interested in is the relationship between how much of the movie is revealed by the trailer and the type of movie that is being advertised. If you think … Continue reading Detecting Common Story Arcs in Movies

I was a recent victim of an internet fraud. I want to document it here so that I can share this with other consumers as well as the companies that are unknowingly involved. I think this also raises an interesting ethical debate for advertising platforms. The story I recently needed to change an existing United … Continue reading Google Ads Airline Fraud

EVENT ANNOUNCEMENT There will be a data science meetup on Weds. 3/6 at 6:30 pm. It will be located at CoWork Oasis. Dr. Gustavo Arriaga will be giving a talk/demo about identifying hotel renovations using machine learning. Topics covered include: NLP techniques (phrase modeling, topic modeling, etc.) Classification methods (Random forests, neural networks, etc.) Refreshments … Continue reading Identifying hotel renovations using machine learning