Survey on the Objectives of Recommender Systems: Measures, Solutions, Evaluation Methodology, and New Perspectives

Abstract

Recently, recommender systems have played an increasingly important role in a wide variety of commercial applications to help users find favourite products. Research in the recommender system field has traditionally focused on the accuracy of predictions and the relevance of recommendations. However, other recommendation quality measures may have a significant impact on the overall performance of a recommender system and the satisfaction of users. Hence, researchers’ attention in this field has recently shifted to include other recommender system objectives. This article aims to provide a comprehensive review of recent research efforts on recommender systems based on the objectives achieved: relevance, diversity, novelty, coverage, and serendipity. In addition, the definitions and measures associated with these objectives are reviewed. Furthermore, the article surveys the evaluation methodology used to measure the impact of the main challenges on performance and the new applications of the recommender system. Finally, new perspectives, open issues, and future directions are provided to develop the field.

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

“…Applications in a smart city work through a digital infrastructure that can connect different systems and devices [27,85].…”

“…Mobility and Transport: In the process of implementing these technologies in smart cities, mobile applications have had a great impact, among which those related to mobility and transportation stand out [1,39,74,85,86]. Applications used worldwide, such as Uber, Lyft, or Citymapper, allow users to find public transport routes, book shared rides, or even rent bicycles or electric scooters.…”

“…Broadly speaking, these platforms typically make use of technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence, and sensors to collect, process, and analyse real-time data related to multiple aspects of the city [7,39]. Applications in a smart city work through a digital infrastructure that can connect different systems and devices [27,85].…”

Research directions in recommender systems for health and well-being

“…However, as the Web 2.0 and beyond (Anderson 2007) came into being, recommender systems became ever more so present in online systems and began to appear in health-related systems (Mankoff et al 2002;Cawsey et al 2007;Turoff and Hiltz 2008). As the Web has matured and developed, so have recommender systems, and specifically recommender system applications in health (Alhijawi et al 2022). Recent applications include drug recommendation (Zheng et al 2022), diabetes treatment recommendation (Oh et al 2022), cancer drug response prediction (Wang et al 2022), and recommendations for cancer patients and caregivers (Rahdari et al 2022), to name a few.…”

Individual Persistence Adaptation for User-Centric Evaluation of User Satisfaction in Recommender Systems

“…Recommender systems (RS) play a vital role in various domains such as commerce, entertainment, and education, supporting information systems by presenting items or information without explicit queries. The ultimate objective of RS is to enhance user satisfaction, benefiting both the users themselves and the service providers [1], [2]. However, a fundamental question arises: How can satisfaction be effectively measured?…”