# RecPOID: POI Recommendation with Friendship Aware and Deep CNN
## 1 Introduction  
As a result of the explosive growth of smartphones, location-based social networks (LBSNs) such as Yelp and location-based service providers like Foursquare are popular. By 2020, Foursquare has over 55 million users per month and over three billion monthly visits to various locations worldwide; the Swarm app has nine million check-ins per day. LBSN also allows users to share information and find friends. These Location-based social networks collected users' check-in data such as users' tips at the location and visited locations' geographical information (longitude and latitude) [1, 2]. To increase the user experience in LBSNs, from mining social relationships and users' check-in history, point-of-interest (POI) recommendation is recommended, which advocates unknown places on users for visiting. POI recommendation a critical task that will create the most significant results in LBSNs, which allows users to explore new favorite places in the LBSNs. POI recommendation usually explores venue information such as users' social relationships and categories to recommend a list of POIs where users most probably visit locations that had been checked-in by their friends. POI recommendation improves a user's interest in LBSN service and profits advertising agencies through planning, launching, and analyzing a successful marketing campaign. Specifically, users can discover nearby downtown shopping malls and restaurants in Foursquare. The quality of LBSN services and benefits both POI owners and users can be greatly improved by personalized POI recommendations. For instance, users can discover POIs that they potentially favor [3, 4, 5]. In former studies, has been shown that POI recommendation is meaningfully controlled by three significant items: friend importance [6], check-in correlation [7], and userpreference [8]. Friend importance appraises on the user' friend influence to go and visit a location. User preference outlines the similarity between potential willingness and users to the specific POIs in a region of interest. Common interests among users lead to a potential possibility to visit the same POI. The behavior of users is correlated based on the POIs and the check-in. For instance, users tend to travel several scenic spots or go to nearby locations with short distances. Previous research reports one of these factors or does not adequately describe them. In POI recommendation systems, the friend's importance is a vital issue because human beings are commonly living together as social and groups. The user check-in activities of his friends are very influential [8]. However, most recent studies in location-based recommendation systems consider all social friends, although the survey of check-in pattern's most similar friendship is important to accurate prediction and recommendation according to the user's interests [9]. In recent years, there has been a constant rise in utilizing the deep learning pipeline to artificial intelligence tasks such as POI recommendation, natural language processing, and also computer vision, where key features can be extracted deeply and effectively [10; 11; 12; 13]. Deep learning is a representation-learning approach that can learn from data representations with multiple simple modules. Each module explores higher-level representations of input (former module) from lower-level data [14; 15]. Hence, different deep learning structures are very good at extracting intrinsic high-level features beneficial for recommendation tasks. However, few studies have been proposed on location-based recommender systems with the aid of artificial intelligence and deep learning that incorporate key factors such as exploring the patterns of check-in behaviors and the impact of most similar friendships and user preferences. [PERSON] et al. [16] have designed a Spatial-Aware Hierarchical Collaborative Deep Learning technique (SH-CDL) used to spatial-aware personal preferences of hierarchically additive representation learning and heterogeneous features.