A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization

TLDR: The SSTPMF model performs better in alleviating the cold start problem than state-of-the-art methods in terms of normalized discount cumulative gain on both data sets and the results obtained from two real data sets show that taking POI correlation and user similarity into account can further improve recommendation performance.

Abstract: In recent years, point of interest (POI) recommendation has gained increasing attention all over the world. POI recommendation plays an indispensable role in assisting people to find places they are likely to enjoy. The exploitation of POIs recommendation by existing models is inadequate due to implicit correlations among users and POIs and cold start problem. To overcome these problems, this work proposed a social spatio-temporal probabilistic matrix factorization (SSTPMF) model that exploits POI similarity and user similarity, which integrates different spaces including the social space, geographical space and POI category space in similarity modelling. In other words, this model proposes a multivariable inference approach for POI recommendation using latent similarity factors. The results obtained from two real data sets, Foursquare and Gowalla, show that taking POI correlation and user similarity into account can further improve recommendation performance. In addition, the experimental results show that the SSTPMF model performs better in alleviating the cold start problem than state-of-the-art methods in terms of normalized discount cumulative gain on both data sets.