# On Gossip-based Information Dissemination in Pervasive Recommender Systems

## TL;DR

This paper proposes Propagate and Filter, a decentralized method for smartphone-based information exchange in pervasive recommender systems, addressing privacy and data scarcity issues, with preliminary results demonstrating technical feasibility.

## Contribution

It introduces a novel decentralized approach for device-to-device preference sharing in pervasive systems, enhancing privacy and context-aware recommendations.

## Key findings

- Average ad-hoc connection delay of 25.9 seconds
- Reliable connection success within 6 meters
- Prototype implementation shows technical feasibility

## Abstract

Pervasive computing systems employ distributed and embedded devices in order to raise, communicate, and process data in an anytime-anywhere fashion. Certainly, its most prominent device is the smartphone due to its wide proliferation, growing computation power, and wireless networking capabilities. In this context, we revisit the implementation of digitalized word-of-mouth that suggests exchanging item preferences between smartphones offline and directly in immediate proximity. Collaboratively and decentrally collecting data in this way has two benefits. First, it allows to attach for instance location-sensitive context information in order to enrich collected item preferences. %enhance on-device recommendations. Second, model building does not require network connectivity. Despite the benefits, the approach naturally raises data privacy and data scarcity issues. In order to address both, we propose Propagate and Filter, a method that translates the traditional approach of finding similar peers and exchanging item preferences among each other from the field of decentralized to that of pervasive recommender systems. Additionally, we present preliminary results on a prototype mobile application that implements the proposed device-to-device information exchange. Average ad-hoc connection delays of 25.9 seconds and reliable connection success rates within 6 meters underpin the approach's technical feasibility.