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Supporting Knowledge Workers through Personal Information Assistance with Context-aware Recommender Systems

Year: 2024

Type: conference-paper

Abstract: Recommender systems are extensively employed across various domains to mitigate information overload by providing personalized content. Despite their widespread use in sectors such as streaming, e-commerce, and social networks, utilizing them for personal information assistance is a comparatively novel application. This emerging application aims to develop intelligent systems capable of proactively providing knowledge workers with the most relevant information based on their context to enhance productivity. In this paper, we explore this innovative application by first defining the scope of our study, outlining the key objectives, and introducing the main challenges. We then present our current results and progress, including a comprehensive literature review, the proposal of a framework, the collection of a pioneering dataset, and the establishment of a benchmark for evaluating a recommendation scenario on our published dataset. We also discuss our ongoing efforts and future research directions.

Source: ACM Conference on Recommender Systems (RecSys)

DOI: 10.1145/3640457.3688010

Author: Mahta Bakhshizadeh

Institutions: German Research Centre for Artificial Intelligence, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung

Awards: Nachhaltige evolutionäre Unternehmensgedächtnisse: Methoden und Effekte von Managed Forgetting für die administrative Wissensarbeit, G8254455972