# A Personalized Recommender System for Pervasive Social Networks

arXiv: https://arxiv.org/abs/2205.15063 · HTML: https://mlsanity.com/paper/2205.15063/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2022.

## Built on by (most cited first, 4 of 4)

- [Recommender Systems for Online and Mobile Social Networks: A survey](https://mlsanity.com/paper/2307.01207/) (2023)
- [Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data](https://mlsanity.com/paper/2306.16029/) (2023)
- [MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge](https://mlsanity.com/paper/2306.15990/) (2023)
- [Context-Aware Configuration and Management of WiFi Direct Groups for Real Opportunistic Networks](https://mlsanity.com/paper/2307.03126/) (2023)

## Abstract (arXiv)

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.