An approach for proactive mobile recommendations based on user-defined rules

Ilarri, Sergio (Universidad de Zaragoza) ; Trillo-Lado, Raquel (Universidad de Zaragoza)

Resumen: In the Big Data era, context-aware mobile recommender systems are crucial in assisting citizens and tourists in making informed decisions, providing a suitable way for users to find the relevant data. These systems should be proactive, able to detect the ideal time and location to provide recommendations for a specific item or activity. To accomplish this, push-based recommender systems can be employed, utilizing context rules to determine when a recommendation should be initiated. However, there is very limited reported experience in defining and implementing such systems and a complete generic solution that adapts flexibly to the preferences of users and protects their privacy is still missing.

In this paper, we present a novel approach where appropriate types of recommendations are provided automatically, without the need for user input. Our proposal allows users to easily activate, deactivate, customize, and create rules for improved personalization. Additionally, the module that, based on the context, decides the types of recommendations required is executed on the user’s mobile device, reducing wireless communication and safeguarding the user’s privacy, as context data are evaluated locally. To illustrate the approach, we have developed R-Rules, a prototype for Android devices focused on the triggering of recommendation rules, which provides a friendly user interface that facilitates user personalization. We have evaluated various technological options and demonstrated the feasibility, performance, and scalability of the proposal, as well as its suitability to users’ needs.

Idioma: Inglés

DOI: 10.1016/j.eswa.2023.122714

Año: 2024

Publicado en: Expert Systems with Applications 242 (2024), 122714 [22 pp.]
ISSN: 0957-4174

Factor impacto JCR: 7.5 (2024)

Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 28 / 204 = 0.137 (2024) - Q1 - T1
Categ. JCR: OPERATIONS RESEARCH & MANAGEMENT SCIENCE rank: 7 / 106 = 0.066 (2024) - Q1 - T1
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 36 / 366 = 0.098 (2024) - Q1 - T1

Factor impacto SCIMAGO: 1.854 - Artificial Intelligence (Q1) - Computer Science Applications (Q1) - Engineering (miscellaneous) (Q1)

Tipo y forma: Article (Published version)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)