# Event-Based Probabilistic Embedding for POI Recommendation
###### Abstract  
Location-based social networks (LBSNs) have collected massive geo-tagged information, enabling the derivation of user preference for point of interests (POIs) in support of personalized recommendation. The existing embedding techniques deal with multiple factors by embedding a separate model for each factor. As a result, the interaction amongst various factors cannot be captured properly. In addition, we notice that the effectiveness of personalized recommendation is closely related to the current time and location. It is obvious that users would check into a POI which fits their interests, even if the current location is far away from the POI or the time is inappropriate. Therefore, it is necessary to recommend the right POI according to the time and geographic location of the user. In other words, it is necessary to predict the most likely visiting event, including users, POI, event time, and event location. In this paper, we propose a probabilistic embedding model called Topic And Region Embedding (TARE), which embeds events by simulating the users' decision-making process. The results of TARE not only take various factors and their interaction into consideration but also consider the time and geographic location of events. Extensive experiments on three location-based social network datasets show that TARE achieves better performance in recommendation accuracy than existing state-of-the-art methods. POI recommendation; probabilistic generation model; deep neural network; probabilistic embedding +
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so on [11; 12; 13; 14]. The major ideas behind these systems include two steps. The first step is to obtain user and POI embeddings. The second step takes the POI and user embedding into a non-linear model [8] or a linear model [15] for further joint training. There are two main types of embedding algorithms. The first one is a variant of the word2 vec algorithm [9; 10]. It generates the POI sequence according to the check-in time, and then, it uses the POI sequence to embed the POI. The other one is a variant of the node2 vec algorithm [8; 16; 17]. It constructs a graph of users and POIs, such as the user's social network graph, the geographical graph of the POI, and so on, in order to use the graphs to perform POI embedding. Nevertheless, these existing embedding methods have certain deficiencies. Firstly, the existing embedding methods consider multiple factors by embedding a separate model for each factor without exploring the intrinsic relationship between different factors. Notice that the intrinsic relationship between different factors has a great impact on the user's behavior. For example, users may like to visit a POI near their home at night while preferring to visit a POI near their work location during the day (time affects the user's geographic preferences). Second, we notice that the effectiveness of personalized recommendation is closely related to the current time and location. The certain time and user history check-in records can be used to infer the user preferences at event time. Event locations can filter out POIs that are too far away and thus improve the accuracy of user behavior predictions. So, the embedding methods should take into account the time and geographic location for better recommendation performance. To address the above issues in existing methods, we propose a novel embedding method called Topic And Region Embedding (TARE), which takes into account a variety of factors including geographical effect, temporal effect, user preference and POI categories. Unlike the existing embedding method, the TARE embedded object is not a user or a POI but an event that includes user, POI, event time and event location. The results obtained by TARE not only take into account user preferences and POI information but also event time and event locations. To explore the intrinsic link between multiple factors, we use the probability generation model to embed events. The basic idea is to \"mimic\" user behaviors in a process of decision making.