Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

TL;DR

This paper introduces a decentralized matrix factorization framework for POI recommendation that enhances privacy and reduces computational costs by training models locally on user devices using a random walk-based method.

Contribution

The paper proposes a novel decentralized training approach for matrix factorization in POI recommendation, addressing privacy concerns and computational inefficiencies of centralized models.

Findings

Significant improvements in recommendation precision and recall.

Effective decentralized training without exposing user data.

Reduced computational and storage requirements.

Abstract

Points of interest (POI) recommendation has been drawn much attention recently due to the increasing popularity of location-based networks, e.g., Foursquare and Yelp. Among the existing approaches to POI recommendation, Matrix Factorization (MF) based techniques have proven to be effective. However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices. (2) Privacy issues: the users' preferences are at risk of leaking to malicious attackers via the centralized learner. To solve these, we present a Decentralized MF (DMF) framework for POI recommendation. Specifically, instead of maintaining all the low rank matrices and sensitive rating data for training, we propose a random walk based decentralized training technique to train MF models on each user's end, e.g., cell phone and Pad. By doing so, the ratings of each user are still kept on one's own hand, and moreover, decentralized learning can be taken as distributed learning with multi-learners (users), and thus alleviates the computation and storage issue. Experimental results on two real-world datasets demonstrate that, comparing with the classic and state-of-the-art latent factor models, DMF significantly improvements the recommendation performance in terms of precision and recall.