Affective Impression: Sentiment-Awareness POI Suggestion via Embedding in Heterogeneous LBSNs

Location-based social networks (LBSNs) add geographical information into traditional social networks and link people’s virtual and physical lives. As an important application of LBSNs, point-of-interest (POI) suggestion has become an important method to help users explore interesting and attractive locations in LBSNs. The main problems of POI suggestion include data sparsity and cold start, which have been paid much attention by existing techniques. There are two major challenges which can greatly influence the performance of suggestion accuracy. One is the fuzzy boundary between sentiments, i.e., the fine distinction between sentiments makes it difficult to classify words and texts after word-sentiment mapping operation. The other challenge is the unreliability of data quality represented by similarity metrics, which relies on data integrity and path reachability of a heterogeneous network. To cope with the above two challenges, we present a novel framework called Community-based Sentiment Extraction and Network Embedding for POI Recommendation (CENTER) for suggesting impressive POIs to a specific user in an effective fashion. The CENTER framework contains two essential techniques: (1) a latent probabilistic generative model called Community-based Sentiment Extraction (CSE), which can accurately capture the sentiments from review content in LBSNs by taking into consideration the characteristics of social communities. The parameters of the CSE model can be inferred effectively by the Gibbs sampling method. The primary sentiments are obtained based on the distribution of sentiments; (2) a network embedding model called Sentiment-aware Nework Embedding for POI Recommendation (SNER) is employed to learn the representation of the factors including POIs, users and textual sentiments in a low-dimensional embedding space. The joint training is utilized to alternatively sample all sets of edges in a heterogeneous information network. Extensive experiments were conducted on two large-scale real datasets, in order to evaluate the performance of the proposed CENTER framework. The results demonstrate that CENTER is superior to the state-of-the-art baseline methods in the effectiveness and efficiency of POI suggestion.