WhoSNext: Recommending Twitter Users to Follow Using a Spreading Activation Network Based Approach

Risultato della ricerca: Conference contribution

6 Citazioni (Scopus)


The huge number of modern social network users has made the web a fertile ground for the growth and development of a plethora of recommender systems. To date, recommending a new user profile X to a given user U that could be interested in creating a relationship with X has been tackled using techniques based on content analysis, existing friendship relationships and other pieces of information coming from different social networks or websites. In this paper we propose a recommending architecture-called WhoSNext (WSN)-tested on Twitter and which aim is promoting the creation of new relationships among users. As recent researches show, this is an interesting recommendation problem: for a given user U, find which other user might be proposed to U as a new friend. Instead of conducting a study based on a semantic approach (e.g. analyzing tweet content), the proposed algorithm exploits a graph created from a set of Twitter users called seeds. In this work-and, to the best of our knowledge, for the first time-this issue is addressed using only user ID for building a particular Spreading Activation Network. This network was firstly trained and then tested on a set consisting of over 400,000 real users. Experimental results show that this approach outperforms the results obtained from many well-known state-of-the-art systems, which are much more expensive in terms of either data preprocessing or computational resources.
Lingua originaleEnglish
Titolo della pubblicazione ospiteIEEE International Conference on Data Mining Workshops, ICDMW
Numero di pagine9
Stato di pubblicazionePublished - 2020

Serie di pubblicazioni


All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.1700.1706???
  • ???subjectarea.asjc.1700.1712???


Entra nei temi di ricerca di 'WhoSNext: Recommending Twitter Users to Follow Using a Spreading Activation Network Based Approach'. Insieme formano una fingerprint unica.

Cita questo