Real-time event embedding for POI recommendation

NRDL: Decentralized user preference learning for privacy-preserving next POI recommendation

With the rapid development of mobile devices, global positioning system and Web2.0 technology, more and more users are choosing to share rich information in location-based social networks. Point of interest (POI) recommendation is a hot research direction in recent years, this paper first based on the existed recommending research results on the POI, using methods of linear weighting and cascading combination to integrate social factors, geographical factors and time factors to recommend POI, for the sake of improving effectiveness of results. Then, in the Spark cluster environment, it was extended to improve the efficiency and scalability of POI recommendation. Experimental results showed that the implementation of comprehensive POI recommendation methods in Spark cluster environment was more efficient, as the amount of data increased, efficiency advantage was increasingly obvious.

New developments in mobile computing, wireless networking and social networking leverage opportunities to create location-based social networks (LBSNs) such as Foursquare, Gowalla, Facebook Place. Pointof-interest (POI) recommendation is a significant task in LBSNs, since it provides recommendations that respect personal preferences and capture social and environmental parameters. Moreover, employing Wireless Sensor Networks (WSNs) in POIs enables to improve recommendation accuracy. Most of existing POI recommendation methods deals with the only single domain, whereas the use of information available from other application domains potentially provides better POI recommendation results. This is particularly relevant for the newly launched LBSNs, where little check in information is available. In this paper, to the best of our knowledge, we are the first to propose a cross domain POI recommendation method in LBSNs and apply transfer learning for this purpose. To this end, we first incorporate both user and POI knowledge in auxiliary domains by discovering the principle coordinates. Next, we transfer them to the target domain and then make predictions using the transferred knowledge. The experimental results demonstrate high performance of our method and the merits of cross-domain POI recommendation in LBSNs.

The rising prosperity of Location-based Social Networks (LBSNs) witnessed an explosion in the availability of geo-tagged social media data, which enables tremendous location-aware online services, especially next point of interest (POI) recommendation. However, previous next POI recommendation studies usually adopt fix-length time windows for user check-in sequence modeling, leading to a limited capacity in capturing fine-grained user temporal preferences that easily change over time. Besides, existing methods often directly leverage multi-modal contexts as auxiliary to alleviate the data sparsity issue, which fails to fully exploit the sequential patterns of contextual information for inferring user interest drift. To address the above challenges, we propose a novel framework named iTourSPOT which extends traditional collaborative filtering methods with a context-aware POI embedding architecture. For enhancing temporal interests modeling capacity, we associate the context feature extraction with varying-length sessions and incorporate check-in frequencies of POIs as prior knowledge to instruct the session representation learning of our model. Moreover, a collaborative sequence transduction model is designed for joint context sequence modeling and session-based POI recommendation. Experimental results on a real-world geo-tagged photo dataset clearly demonstrate the effectiveness of the proposed framework when compared with state-of-the-art baseline methods, especially in both sparse and cold-start scenarios.

Point of Interest (POI) recommendation is to use the user check-in data in location-based Social Networks (LBSN) to predict the next Location that the user will visit. With the rapid development of LBSN, users' check-in information (rating information, geographic location, check-in sequence, social network, etc.) has become more and more accessible. How to effectively fuse and model these multi-source heterogeneous data is crucial to personalized POI recommendations. This paper presents a POI recommendation model based on improved factorization machine and Bert. By integrating social influence on the basis of factorization machine, Bert was used to extract the check-in sequence features. Experiments on real data sets show that our model performs better than the traditional POI recommended model.

THE popularity of location-aware devices such as smart phones makes users freely share their activities through various location-based social networks (LBSNs), such as Foursquare and Yelp. A large amount of user-contributed data enable to develop effective point-of-interest (POI) recommender systems. It not only guides users to explore more interesting attractions, but also helps the location service providers deliver targeted advertising. Now most of existing studies focus on recommending POIs in the same city or region, named as traditional POI recommender systems. However, they fail to deal with the increasingly popular case: users travel to new cities to explore more attractions. This raises the problem that how we shall recommend POIs in a target city to a new visitor based on her/his check-in records in source cities. We refer to this problem as crossing-city POI recommendations. Compared with traditional POI recommender systems, crossing-city POI recommender systems are more challenging due to the following aspects:

Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days.

Recommending a limited number of Point-of-Interests (POIs) a user will visit next has become increasingly important to both users and POI holders for Location-Based Social Networks (LBSNs). However, POI recommendation is a challenging task since complex sequential patterns and rich contexts are contained in extremely sparse user check-in data. Recent studies show that embedding techniques effectively incorporate POI contextual information to alleviate the data sparsity issue, and Recurrent Neural Network (RNN) has been successfully employed for sequential prediction. Nevertheless, existing POI recommendation approaches are still limited in capturing user personalized preference due to separate embedding learning or network modeling. To this end, we propose a novel unified spatio-temporal neural network framework, named PPR, which leverages users’ check-in records and social ties to recommend personalized POIs for querying users by joint embedding and sequential modeling. Specifically, PPR first learns user and POI representations by joint modeling User-POI relation, sequential patterns, geographical influence, and social ties in a heterogeneous graph and then models user personalized sequential patterns using the designed spatio-temporal neural network based on LSTM model for the personalized POI recommendation. Furthermore, we extend PPR to an end-to-end recommendation model by jointly learning node representations and modeling user personalized sequential preference. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms state-of-the-art baselines for successive POI recommendation in terms of Accuracy, Precision, Recall and NDCG. The source code is available at: https://www.anonymous.4open.science/r/DSE-1BEC.

The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Many studies have shown that geographic information plays an important role in POI recommendation. In this study, we focus on two levels geographic information: local similarity and global similarity. We further show that AUC-MF can be easily extended to incorporate geographical contextual information for POI recommendation. Specifically, we propose two novel methods to incorporate geographical information in AUC-MF. Different from most existing models where the contextual information are incorporated into the objective function, the incorporation of contextual information in AUC-MF is a refinement of the model and a sampling strategy. The sampling strategy could speedup convergence and the refining of recommendations is independent of training of the model. This mechanism also enables AUC-MF to be able produce recommendations refined towards different contextual information, with minimum computational cost. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy.

Leveraging graph neural networks for point-of-interest recommendations

Point of Interest (POI) recommender systems help provide their users with a location or place that they might be interested in visiting. When combined with Location-Based Social Networks (LBSNs), POI recommender systems can be restructured to recommend POI for groups of users and not just individuals. The research focused on Group Recommender Systems (GRSs) and specifically, POI GRSs are scarce when compared to recommender systems for individuals. There are two main techniques that are used for POI GRSs, the Group Profiling methods and the Users Score Aggregation methods. Both methods have their drawbacks, as the Group Profiling methods do not recommend well for new groups and the Users Score Aggregation methods generally do not perform as well as the Group Profiling methods for established groups. In this paper, we propose new result aggregation methods that use both the Group Profiling method and the Users Score Aggregation method's results to provide the best POI group recommendations without the aforementioned drawbacks.

Point of Interest (POI) recommendation is one of the important means for businesses to fully understand user preferences and meet their personalized needs, laying a solid foundation for the development of e-commerce and social networks. However, traditional social network POI recommendation algorithms suffer from various problems such as low accuracy and low recall. Therefore, a social network POI recommendation algorithm using the Internet of Things (IoT) and deep reinforcement learning (DRL) is proposed. First, the overall framework of the POI recommendation algorithm is designed by integrating IoT technology and DRL algorithm. Second, under the support of this framework, IoT technology is utilized to deeply explore users' personalized preferences for POI recommendation, analyze the internal rules of user check-in behavior and integrate multiple data sources. Finally, a DRL algorithm is used to construct the recommendation model. Multiple data sources are used as input to the model, based on which the check-in probability is calculated to generate the POI recommendation list and complete the design of the social network POI recommendation algorithm. Experimental results show that the accuracy of the proposed algorithm for social network POI recommendation has a maximum value of 98%, the maximum recall is 97% and the root mean square error is low. The recommendation time is short, and the maximum recommendation quality is 0.92, indicating that the recommendation effect of the proposed algorithm is better. By applying this method to the e-commerce field, businesses can fully utilize POI recommendation to recommend products and services that are suitable for users, thus promoting the development of the social economy.

Points-of-interest (POIs) recommendation plays a vital role in location-based social networks (LBSNs) by introducing unexplored POIs to consumers and has drawn extensive attention from academia and industry. Existing POI recommender systems usually learn fixed latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatio-temporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers' complicated check-in actions. To this end, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe the dynamic nature of POIs. Besides, check-in actions to a POI are also affected by the zone where it is located. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance POI embeddings. Along this line, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ime-z <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</b> ne-space <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> OI embedding model ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ToP</b> ), which integrates multi-knowledge graphs and topic model to introduce not only spatio-temporal effects but also sentiment constraints into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in a different period with spatial constraints via knowledge graph learning. To add sentiment constraints, ToP jointly combines these vectors with the zone's representations learned by topic models to make explainable recommendations. ToP considers the time, space, and sentiment of POI in a unified embedding framework, which benefits the POI recommendations. Extensive experiments on real-world Changchun city datasets demonstrate that ToP achieves state-of-the-art performance in terms of common metrics and provides more insights for consumers' POI check-in actions.

With the development of location-based social networks (LBSNs), users can check in Point-of-Interest (POIs) at any time. However, users do not check in all places they have visited, so the POI trajectory sequence generated through LBSNs is incomplete. An incomplete POI trajectory will have a negative impact on subsequent tasks such as POI recommendation and next POI prediction. Therefore, we complete the missing POI in the user trajectory sequence. Since the POI trajectory sequence is incomplete, it is a challenge to use the pre-order and post-order trajectory sequences with missing POIs. Therefore, we propose a masked POI trajectory model (MPTM) that uses the bidirectionality of BERT to complete the missing POIs in user’s behavior sequence. By masking the missing POIs, MPTM fully explores the relationship between the missing POIs and the known POIs to predict the missing POIs. In order to strengthen the relationship between POIs in the user trajectory sequence, we build a graph for each user’s incomplete POIs sequence to explore the user’s hidden behavior habits. Besides, we design experiments to explore the relationship between the continuity of the number of missing POIs and the predictive ability of the model. The experimental results demonstrate that our MPTM outperforms the state-of-the-art models for completion on missing POIs of user’s behavior sequence.

A point-of-interest(POI) recommendation aims to mine a user’s visiting history and find her/his potentially preferred places. The decision process when choosing a POI is complex and can be influenced by numerous factors, including personal preferences, geographical considerations, and user social relations. While latent factor models have been proven effective and are widely used for recommendations, adopting them to POI recommendations requires delicate consideration of the unique characteristics of location-based social networks (LBSNs). To this end, in this paper, we propose a joint convolution matrix factorization model, named the Review Geographical Social (ReGS) which strategically takes various factors into consideration. Specifically, this model captures geographical influences from a user’s check-in behaviour, and user social relations can be effectively leveraged in the recommendation model. The reviews information available on LBSNs could be related to a user’s check-in action, providing a unique opportunity for a POI recommendation. We model above three types of information under a unified POI recommendation framework based on convolution matrix factorization which integrates a convolutional neural network into a probability matrix factorization. Finally, we conduct a comprehensive performance evaluation for the ReGS using two real-world datasets collected from Foursquare. Experimental results show that the ReGS achieves significantly superior precision and recall rates to other state-of-the-art recommendation models.