  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.
