Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

Next point-of-interest(POI)recommendation is an important personalized task in location-based social networks(LBSNs)and aims to recommend the next POI for users in a specific situation with historical check-in data.State-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural network(RNN)based models for modeling.However,these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate POIs.To address these limitations,a spatiotemporal trajectory(STT)model is proposed in this paper.We use the long short-term memory(LSTM)model with an attention mechanism as the basic framework and introduce the user’s spatiotemporal information into the model in encoding.In the process of encoding information,an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and distance.In addition,we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user’s current trajectory and the candidate set.We evaluate the performance of our STT model with four real-world datasets.Experimental results show that our model outperforms existing state-of-the-art methods.

CTS: Combine Temporal Influence and Spatial Influence for Time-Aware POI Recommendation

As location-based social network (LBSN) services become more popular in people’s lives, Point of Interest (POI) recommendation has become an important research topic.POI recommendation is to recommend places where users have not visited before. There are two problems in POI recommendation: sparsity and precision. Most users only check-in a few POIs in an LBSN. To tackle the sparse problem in a certain extent, we compute the similarity between the check-in datasets of different times. For the precision problem, we incorporate temporal information and geographical information. The temporal information will influence how the user chooses and allow the user to visit different distance point on different day. The geographical information is also used as a control for points which are too far away from the user’s check-in data. Our experimental results on real life LBSN datasets show that the proposed approach outperforms the other POI recommendation methods substantially.

POI Neural-Rec Model via Graph Embedding Representation

With the booming of the Internet of Things(Io T)and the speedy advancement of Location-Based Social Networks(LBSNs),Point-Of-Interest(POI)recommendation has become a vital strategy for supporting people’s ability to mine their POIs.However,classical recommendation models,such as collaborative filtering,are not effective for structuring POI recommendations due to the sparseness of user check-ins.Furthermore,LBSN recommendations are distinct from other recommendation scenarios.With respect to user data,a user’s check-in record sequence requires rich social and geographic information.In this paper,we propose two different neural-network models,structural deep network Graph embedding Neural-network Recommendation system(SG-Neu Rec)and Deepwalk on Graph Neural-network Recommendation system(DG-Neu Rec)to improve POI recommendation.combined with embedding representation from social and geographical graph information(called SG-Neu Rec and DG-Neu Rec).Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network.Finally,we compare the performances of these two models and analyze the reasons for their differences.Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.

Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks

In mobile social networks,next point-of-interest(POI)recommendation is a very important function that can provide personalized location-based services for mobile users.In this paper,we propose a recurrent neural network(RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information(such as time,current location,and friends’preferences).We develop a spatial-temporal topic model to describe users’location interest,based on which we form comprehensive feature representations of user interests and contextual information.We propose a supervised RNN learning prediction model for next POI recommendation.Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach,and achieve best F1-score of 0.196754 on the Gowalla dataset and 0.354592 on the Brightkite dataset.