# RecPOID: POI Recommendation with Friendship Aware and Deep CNN
## 1 Introduction
A new POI recommendation method is employed by [9], capable of mine information in real-time. Convolutional Neural Networks (CNN) are used for mining POI textual information and learn their inherent representation. To improve accuracy, they add real-time POI embedding in the matrix factorization approach. [PERSON] et al. [6] have presented a new modeling user check-in behavior based on focusing on neighborhood competition and area attraction. The visitation through the attractiveness of the area and neighborhood competition (VAN) method has been implemented by incorporating neighborhood competition and area attraction factors. [PERSON] et al. [17] have implemented a POI recommendation model to combine time items, geographical factors, also social factors. Their approach is based on the linear weighting and cascading combination. A deep neural network (DNN) was suggested by [5] to integrate numerous attributes in LBSNs and then learn how they affect user behavior. Moreover, to reduce data sparsity within POI recommendation systems, categorical, geographical, and co-visiting influences have been exploited. The purpose of the study by [PERSON] et al. [18] is to obtain a predictive score for each user of any POI. This overall score is the individual prediction score by factors such as user preference modeling, importance of friend modeling, and check-in correlation modeling. [PERSON] et al. [19] By supporting information on location-based social networks, consider the three factors of visited venues, the user's geographical location, and social influence. For prediction and recommendation to visit the new POI, they used characteristics such as the distance proximity of previously visited locations by users and the impact of social relationships. The main function of the research [PERSON] et al. [20] is to investigate the patterns of the check-in orders of all clients and to consider the consecutive POIs visited dynamically. This work predicts the probability of the sequence of visiting locations according to the geographical location and social scores. Due to the importance of recommending POI to users in LBSNs and predicting the user's next possible locations with higher accuracy, and suggesting places of interest to the user, and completing the shortcomings of past methods, this research has proposed a new deep learning structure named RecPOID to recommend top-k POIs to each user. RecPOID is a combination of convolutional neural network and c-mean fuzzy clustering method,which first identifies the most similar friendship relying on the check-in behavioral pattern of user friends and second, CNN's proposed model based on six input features including user's ID, month, day, hour, minute, and second predicts the next POI locations to visit, depending on the current position and time of the user. Finally, to increase the proposed POI accuracy, the shortest time distance is measured with a similar friendship check-in pattern, and the next location is recommended to the user. The remaining document is arranged as follows: A POI clustering approach is explained in Section 2.1. Section 2.2 describes the convolutional neural network architecture and in Section 2.3, the proposed CNN model is represented. The results and final remarks are outlined in Section 3.