Designing Recommendation or Suggestion Systems : Looking to the Future

Abstract

A Recommendation or Suggestion System (RSS) helps on-demand digital content and social media platforms identify associations amongst large amounts of transaction data, which are then used to provide personalised viewing and shopping recommendations to consumers. This preface introduces how RSSs are used in the marketplace and various purposes it serves. This paper is a contribution to the ongoing research beyond content-based recommender system. It presents an examination of how the Collective Intelligence Social Tagging System makes a fundamental difference to content-based recommender systems and a suggested hybrid approach to RSS architecture which uses crowdsourcing and tagging to increase the accuracy of content-based RSSs. The lack of a huge data repository for online and e-commerce enterprises restricts the effectiveness of the content-based approach, so RSS research must aim to address these issues and propose a novel approach which incorporates the best of both methods. This preface also introduces three articles which present alternative approaches to effective recommendation approaches. On the social dimension, the use of invasive methods which capture user profile in order to influence behaviour, have opened a pandora’s box of legal and ethical considerations. The design of future RSS cannot ignore these constraints.