With the development of Location-Based Social Network （LBSN） Point-Of-Interest （POI） recommendation， an effective way to alleviate information overload， has attracted much attention. As user check-in data are implicit feedback data and very sparse， a hybrid POI recommendation model based on geographic preference ranking was proposed to effectively capture the user preference for POIs from check-in data. First， considering the implicit feedback characteristics of check-in data and the spatial constraint of user activities， by calculating the influence of POI distances on POI ranking based on the traditional Bayesian personalized Ranking （BPR） model， a weighted BPR model named GWBPR （Geo-Weighted Bayesian Personalized Ranking） was proposed. Then， aiming at the sparsity of user check-in data， by further integrating Logistic Matrix Factorization （LMF） model with GWBPR model， a hybrid model GWBPR-LMF （GWBPR with LMF） was proposed. Experimental results on two real datasets， Foursquare and Gowalla， show that GWBPR-LMF model outperforms the comparison models like BPR， LMF and SAE-NAD （Self-Attentive Encoder and Neighbor-Aware Decoder）. Compared with the relatively good-performance model SAE-NAD， GWBPR-LMF model improves the precision， recall， F1 score， mean Average Precision （mAP） and Normalized Discounted Cumulative Gain （NDCG） by 44.9%， 57.1%， 78.4%， 55.3%， and 40.0% averagely and respectively on Foursquare dataset， and 3.0%， 6.4%， 4.6%， 11.7%， and 4.2% averagely and respectively on Gowalla dataset.

The spatial co-location pattern is a subset of spatial features whose instances frequently appear together in the neighborhoods. Co-location pattern mining methods usually assume that spatial instances are independent to each other， adopt a participation rate， which is the frequency of spatial instances participating in pattern instances， to measure the importance of spatial features in the co-location pattern， and adopt a participation index， which is the minimal participation rate of spatial features， to measure the interest of patterns. These methods neglect some important relationships between spatial features. Therefore， the co-location pattern with dominant feature was proposed to reveal the dominant relationship between spatial features. The existing method for mining co-location pattern with dominant feature is based on the traditional co-location pattern mining and its clique instance model. However， the clique instance model may neglect the non-clique dominant relationship between spatial features. Motivated by the above， the dominant feature mining of spatial sub-prevalent co-location patterns was studied based on the star instance model to better reveal the dominant relationship between spatial features and mine more valuable co-location patterns with dominant feature. Firstly， two metrics to measure feature’s dominance were defined. Secondly， an effective algorithm for mining co-location pattern with dominant feature was designed. Finally， the experimental results on both synthetic and real datasets show that the proposed mining algorithm is efficient and the co-location pattern with dominant feature is pratical.