In today's online world, third parties collect and store your browsing data at staggering rates. Third parties benefit greatly from this information, but do you get fair returns for sharing your data online?
New research from the University of Florida Matherly Professor Anuj Kumar and Santa Clara University's Xiang (Shawn) Wan (UF Ph.D. '22) proposed a novel method to measure the value consumers get from product recommendation systems (RS), one of the most prominent online tools that use consumer data.
While numerous studies have established that RS benefits retailers through increased sales, its benefits to consumers are unclear.
Presumably, RS algorithms are designed to help consumers find products that provide them with higher value among numerous choices. Consumers may value a product because of its higher quality (such as better material and craftsmanship), lower price (for a given quality), or better match with their tastes (such as their color choice). However, how much a consumer values a product is only known to her and not observed by others.
For example, imagine you are searching for a women's top on Macy's website. After searching, you purchased a red v-neck fitted top that you found organically (without RS) and a maroon collared top with a loose fit that you found with RS. The relative values of the two tops are only known to you. Maybe you liked the maroon color more than red or the loose fit more than the tight fit. Or, you attached a higher value (higher quality) to the brand/designer of the maroon top than the red top. However, these facts were not apparent to the online retailer.
The difference in desirability (value) of products you found with and without RS is the true value of the RS. However, since no one observes the value you derive from these products, measuring how valuable RS is to consumers is a challenge.
Kumar and Wan, along with Xitong Li at HEC Paris, propose a novel method of measuring the value of RS to consumers from the similarity scores between products (called affinity scores) computed by RS algorithms in a forthcoming research article in Management Science.
