Personality-aware followee recommendation algorithms: An empirical analysis. (May 2016)

• Record Type: Journal Article
• Authors: Tommasel, Antonela; Corbellini, Alejandro; Godoy, Daniela; Schiaffino, Silvia
• Is Part Of: Engineering applications of artificial intelligence. Volume 51(2016:Mar.)
• Page Start: 24
• Page End: 36
• Publication Date: 2016-05
• Subjects: Followee recommendation -- Twitter -- Human aspects recommendation -- Personality traits; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.01.016
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: As the popularity of micro-blogging sites, expressed as the number of active users and volume of online activities, increases, the difficulty of deciding who to follow also increases. Such decision might not depend on a unique factor as users usually have several reasons for choosing whom to follow. However, most recommendation systems almost exclusively rely on only two traditional factors: graph topology and user-generated content, disregarding the effect of psychological and behavioural characteristics, such as personality, over the followee selection process. Due to its effect over people׳s reactions and interactions with other individuals, personality is considered as one of the primary factors that influence human behaviour. This study aims at assessing the impact of personality in the accurate prediction of followees, beyond simple topological and content-based factors. It analyses whether user personality could condition followee selection by combining personality traits with the most commonly used followee predictive factors. Results showed that an accurate appreciation of such predictive factors tied to a quantitative analysis of personality is crucial for guiding the search of potential followees, and thus, enhance recommendations. Abstract : Highlights: The impact of personality in the accurate prediction of followees is assessed. Personality was quantitatively assessed and combined with common recommendation factors. The combination of predicted factors was inserted into a recommendation algorithm. Adding personality can significantly enhance recommendation precision. Personality should be considered as a distinctive factor in followee selection.