CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things

TL;DR

This paper introduces CRUC, a collaborative filtering-based scheme designed to address the cold-start problem in IoT, enabling better user preference acquisition and personalized services.

Contribution

The paper presents a novel CRUC scheme that effectively solves the cold-start problem in IoT through formulation, filtering, and prediction steps.

Findings

CRUC efficiently solves the cold-start problem in IoT.

Experimental results demonstrate improved recommendation accuracy.

The scheme performs well in real case and simulation environments.

Abstract

The Internet of Things (IoT) aims at interconnecting everyday objects (including both things and users) and then using this connection information to provide customized user services. However, IoT does not work in its initial stages without adequate acquisition of user preferences. This is caused by cold-start problem that is a situation where only few users are interconnected. To this end, we propose CRUC scheme - Cold-start Recommendations Using Collaborative Filtering in IoT, involving formulation, filtering and prediction steps. Extensive experiments over real cases and simulation have been performed to evaluate the performance of CRUC scheme. Experimental results show that CRUC efficiently solves the cold-start problem in IoT.