12 citations · 25 across the 14 of their papers we have counts for
4 papers · 1 filter
A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions
Vinaik Chhetri, Yousaf Reza, Moghis Fereidouni +3
Modern recommendation systems typically follow two complementary paradigms: collaborative filtering, which models long-term user preferences from historical interactions, and conve…
INTERPOS: Interaction Rhythm Guided Positional Morphing for Mobile App Recommender Systems
M. H. Maqbool, Moghis Fereidouni, Umar Farooq +2
The mobile app market has expanded exponentially, offering millions of apps with diverse functionalities, yet research in mobile app recommendation remains limited. Traditional seq…
MobileRec: A Large-Scale Dataset for Mobile Apps Recommendation
M. H. Maqbool, Umar Farooq, Adib Mosharrof +2
Recommender systems have become ubiquitous in our digital lives, from recommending products on e-commerce websites to suggesting movies and music on streaming platforms. Existing r…
Proactive Prioritization of App Issues via Contrastive Learning
Moghis Fereidouni, Adib Mosharrof, Umar Farooq +1
Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to…