Challenges for Anonymous Session-Based Recommender Systems in Indoor Environments

In the last two decades, recommender systems have become more popular since they can provide personalized recommendations in different fields. However, the current research landscape in this area suggests that there is still considerable potential for applying novel recommendation techniques in indoor environments. In addition, the growing attention to privacy raises even more challenges. Anonymous session-based recommender systems represent attractive solutions in this scenario, given their natural predisposition to model the indoor domain by treating each visit to a particular location as an anonymous session. This paper presents some noteworthy challenges regarding several aspects related to the application of these models in indoor environments. We expose our research questions on issues related to the representation of user behavior, cold-start problem, and fairness. Although these problems affect any RS, they become even more challenging in the chosen environment. Finally, we outline a possible use case in a real application scenario to make more transparent and concrete the line of research we intend to pursue in the near future.