Interacting with Recommenders—Overview and Research Directions

TLDR

Automated recommendations are ubiquitous in online shopping, media, and social platforms, yet users typically interact only by inspecting suggested items, with few mechanisms for providing feedback or specifying preferences, limiting the system’s value despite extensive algorithmic research. This paper offers a comprehensive review of the literature on user interaction in recommender systems. It surveys preference‑elicitation and result‑presentation methods, treats recommendation as an interactive process, and discusses real‑world implementations and future research directions.

Abstract

Automated recommendations have become a ubiquitous part of today’s online user experience. These systems point us to additional items to purchase in online shops, they make suggestions to us on movies to watch, or recommend us people to connect with on social websites. In many of today’s applications, however, the only way for users to interact with the system is to inspect the recommended items. Often, no mechanisms are implemented for users to give the system feedback on the recommendations or to explicitly specify preferences, which can limit the potential overall value of the system for its users. Academic research in recommender systems is largely focused on algorithmic approaches for item selection and ranking. Nonetheless, over the years a variety of proposals were made on how to design more interactive recommenders. This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems. We cover existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process. Throughout the work, we furthermore discuss examples of real-world systems and outline possible directions for future works.