Predicting users' future interests on social networks: A reference framework

Fattane Zarrinkalam, Havva Alizadeh Noughabi, Zeinab Noorian, Hossein Fani, Ebrahim Bagheri

Abstract: Highlights•Our work introduces features, formalizes quantifiable measures, and incorporates them into a learn-to-rank framework for predicting future interests.•We empirically evaluate the introduced features within the context of future interest prediction comparing their relevance and impact under varied conditions.•Results evaluate feature performance, both individually and collectively, offering insights into factors influencing specific feature categories’ effectiveness in predicting user interests.