Learning Graph based POI Embedding for Location based Recommendation
With the rapid prevalence of smart mobile devices and the dramatic proliferation of location based social networks (LBSNs) location based recommendation has become an important means to help people discover attractive and interesting points of interest (POIs) However, the extreme sparsity of user POI matrix and cold start issue create severe challenges, causing CF based methods to degrade significantly in their recommendation performance. Moreover, location based recommendation requires spatiotemporal context awareness and dynamic tracking of the user's latest preferences in a real time manner. To address these challenges, we stand on recent advances in embedding learning techniques and propose a generic graph based embedding model, called GE, in this paper. GE jointly captures the sequential effect, geographical influence, temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs (POI POI, POI Region, POI Time and POI Word)into a shared low dimensional space. Then, to support the real time recommendation, we develop a novel time decay method to dynamically compute the user's latest preferences based on the embedding of his/her checked in POIs learnt in the latent space. We conduct extensive experiments to evaluate the performance of our model on two real large scale datasets, and the experimental results show its superiority over other competitors, especially in recommending cold start POIs. Besides, we study the contribution of each factor to improve location based recommendation and find that both sequential effect and temporal cyclic effect play more important roles than geographical influence and semantic effect.
CIKM