Fine-Grained User Profiling for Personalized Task Matching in Mobile Crowdsensing

TL;DR

This paper introduces a personalized task recommender system for mobile crowdsensing that considers user preferences and reliability, improving task matching accuracy over existing centralized methods.

Contribution

It proposes a hybrid preference metric and a semi-supervised learning model to profile user reliability, along with a matrix factorization approach for new tasks.

Findings

System outperforms benchmarks in user profiling

Achieves higher accuracy in personalized task recommendation

Effective in inferring reliability for new tasks

Abstract

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the crowdsensing platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users' preference and reliability. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user's preference and reliability into consideration. We first present a hybrid preference metric to characterize users' preference by exploiting their implicit feedback. Then, to profile users' reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack users' historical information, we further propose a matrix factorization method to infer the users' reliability levels on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to the benchmarks in both user profiling and personalized task recommendation.