Exploration Trade-offs in Web Recommender Systems

One of the main problems of web recommender systems is exposure bias, due to the fact that the web system itself is partly generating its own future, as users can only click on items shown to them. This bias not only creates popularity bias for products but also is one of the main challenges for recommender systems that deal with a very dynamic environment, where new items and users appear frequently and also user preferences change (or the market changes as happened with the coronavirus pandemic). The main paradigm to deal with these changes is to explore and exploit, avoiding the filter bubble effect. However, too much exploration also reduces short-term revenue and hence is usually traffic bounded. In this work, we present a counterfactual analysis that shows that web recommender systems could improve their long-term revenue if significantly more exploration is performed. This is good for the web recommender system but also for everyone as it creates more fair and healthy digital markets. This also improves the web user experience so is a double win-win for the e-commerce platform, the sellers, the users, and ultimately society.