A Personalized Recommender System for Pervasive Social Networks

TL;DR

This paper introduces pPLIERS, a decentralized, personalized recommender system for pervasive social networks that adapts to user interests and operates efficiently with limited network knowledge, demonstrated through simulations in real-world scenarios.

Contribution

The work presents a novel decentralized framework for personalized content recommendation in pervasive social networks, leveraging tag-based reasoning and minimal network knowledge.

Findings

Effective personalization in dynamic environments

Operates with limited network knowledge

Validated through realistic simulations

Abstract

The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. In this work we propose a novel framework for pervasive social networks, called Pervasive PLIERS (pPLIERS), able to discover and select, in a highly personalized way, contents of interest for single mobile users. pPLIERS exploits the recently proposed PLIERS tag based recommender system as context a reasoning tool able to adapt recommendations to heterogeneous interest profiles of different users. pPLIERS effectively operates also when limited knowledge about the network is maintained. It is implemented in a completely decentralized environment, in which new contents are continuously generated and diffused through the network, and it relies only on the exchange of single nodes knowledge during proximity contacts and through device to device communications. We evaluated pPLIERS by simulating its behaviour in three different scenarios: a big event (Expo 2015), a conference venue (ACM KDD 2015), and a working day in the city of Helsinki. For each scenario, we used real or synthetic mobility traces and we extracted real datasets from Twitter interactions to characterise the generation and sharing of user contents.