Dokyun Lee

Will the Global Village Fracture into Tribes: Recommender Systems and Their Effects on Consumers

Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer’s preferences and recommend content best suited to him (e.g., “Customers who liked this also liked…”). A debate has emerged as to whether personalization has drawbacks. By making the web hyper-specific to our interests, does it fragment internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product mix effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations.

When do Recommender Systems Work the Best?: The Moderating Effects of Product Attributes and Consumer Reviews on Recommender Performance

We investigate the moderating effect of product attributes and consumer reviews on the efficacy of a collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top North American retailers website with 184,375 users split into a recommender-treated group and a control group with 37,215 unique products in the dataset. By augmenting the dataset with Amazon Mechanical Turk tagged product attributes and consumer review data from the website, we study their moderating influence on recommenders in generating conversion. We first confirm that the use of recommenders increases the baseline conversion rate by 5.9%. We find that the recommenders act as substitutes for high average review ratings with the effect of using recommenders increasing the conversion rate as much as about 1.4 additional average star ratings. Additionally, we find that the positive impacts on conversion from recommenders are greater for hedonic products compared to utilitarian products while search-experience quality did not have any impact. We also find that the higher the price, the lower the positive impact of recommenders, while having lengthier product descriptions and higher review volumes increased the recommenders effectiveness. More findings are discussed in the Results. For managers, we 1) identify the products and product attributes for which the recommenders work well, 2) show how other product information sources on e-commerce sites interact with recommenders. Additionally, the insights from the results could inform novel recommender algorithm designs that are aware of strength and shortcomings. From an academic standpoint, we provide insight into the underlying mechanism behind how recommenders cause consumers to purchase.

Demand Interactions in Sharing Economies: Evidence from a Natural Experiment Involving Airbnb and Uber/Lyft

We examine whether and how ride-sharing services influence the demand for home-sharing services. Our identification strategy hinges on a natural experiment where Uber/Lyft exited Austin in May 2016 in response to new regulations. On a 12-month longitudinal data spanning 11,423 Airbnb properties, we find Uber/Lyft’s exit led to a decrease of 17.8% in Airbnb demand in Austin. On the supply side, the nightly rate went down by 4.02% and the supplied listings decreased by 6.64%. Further, the geographic demand dispersion of Airbnb decreased and became more concentrated in areas with access to better public transportation. The absence of Uber/Lyft reduced demand more for lower-end properties—whose customers may be more price-sensitive. Further analysis leveraging individual hotel data reveals an increase in Austin hotels’ occupancy in the absence of Uber/Lyft, with a greater increase for hotels that are more substitutable with Airbnb. These results indicate ease of access to transportation in residential areas is critical for the success of home-sharing services. Any policies or regulations that negatively affect ride-sharing services may also negatively affect demand for home-sharing services.

Large Scale Cross Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning

How consumers use review content has remained opaque due to the unstructured nature of text and the lack of review-reading behavior data. The authors overcome this challenge by applying deep learni...Online Word-of-Mouth has great impact on product sales. Although aggregate data suggests that customers read review text rather than relying only on summary statistics, little is known about consumers’ review reading behavior and its impact on conversion at the granular level. To fill this research gap, we analyze a comprehensive dataset that tracks individual-level search, review reading, as well as purchase behaviors and achieve two objectives. First, we describe consumers’ review reading behaviors. In contrast to what has been found with aggregate data, individual level consumer journey data shows that around 70% of the time, consumers do not read reviews in their online journeys; they are less likely to read reviews for products that are inexpensive and have many reviews. Second, we quantify the causal impact of quantity and content information of reviews read on sales. The identification relies on the variation in the reviews seen by consumers due to newly added reviews. To extract content information, we apply Deep Learning natural language processing models and identify six dimensions of content in the reviews. We find that aesthetics and price content in the reviews significantly affect conversion. Counterfactual simulation suggests that re-ordering review content can have the same effect as a 1.6% price cut for boosting conversion.