Beyond Recommender Systems: Helping People Help Each Other

TLDR

The Web offers endless possibilities yet overwhelming choice, prompting the development of recommender systems that help users navigate diverse items by sharing opinions and experiences. The authors aim to present a framework for understanding recommender systems, survey existing approaches, and identify two research challenges: fostering privacy‑respecting communities of interest and integrating multiple information sources for better recommendations. They propose a framework that categorizes and surveys various recommender system approaches.

Abstract

The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other’s experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations.