The Computational Psychology of Digital Shop Assistants

This proposal describes a project lying at the intersection of Computer Science and Social Sciences whose goal is to state and investigate some basic questions concerning recommender systems  (henceforth RS’s).

The advent of the internet has changed cultural markets in profound ways. The global volume of online purchases of music, books, movies, video games and other forms of cultural products has reached the 1.5  trillion dollars mark in 2014, and the trend is increasing. Today, an estimated 1.22 billions people acquire cultural products through the internet.  RS’s are a key component of these online markets. In the old days, a regular customer of, say, a music shop, could get the advice of a knowledgeable shop assistant with whom s/he had developed a relationship of  trust. Based on the knowledge of the customer’s taste and of the music world, the assistant could offer insightful suggestions to the customer, providing useful advice.  Roughly speaking, a RS is a digital, algorithmic analogue of the shop assistant that, on the basis of the past online behaviour of the current customer and of the entire collective behaviour of online visitors, helps navigate the huge catalogue of online choices by providing suggestions in a purely algorithmic fashion.  Thus, a visitor to the YouTube home site will be presented with a list of videos that, hopefully, will match his/her interests, and a person looking for a book on Amazon will likewise see a list of other interesting books to buy. 

In spite of the fact that RS’s are fundamental actors of online cultural markets, their power to shape and influence is still largely unknown. The goal of this proposal is to investigate  the extent to which a cultural market can be affected by RS’s and the interplay between computational and psychological mechanisms underlying them.