PDPNN: Modeling User Personal Dynamic Preference for Next Point-of-Interest Recommendation

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.

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.

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.

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.

Point of interest (POI) recommendation has become an important means to help people discover attractive locations. Previous studies show that modeling the context information of users' check-ins is necessary for POI recommendation. However, most previous hybrid models ignore the fact that a user's interested region is likely to drive him to visit other POIs in it, which means that his region-level interest is indispensable in the recommendation process. In this paper, we take this challenge and propose a hybrid model called POI-Region POI Recommendation (PRPOIR). Specifically, PRPOIR contains two modules: Interest Module and Context Module. In Interest Module, inspired by the success of the Logistic Matrix Factorization (LMF) to model implicit feedback, we apply it to model users' POI-level interest and region-level interest respectively. To capture the difference in the users' emphasis on region-level interests, we innovatively introduce a strategy to establish the dynamic weight of users' region-level interest. Then in Context Module, by further integrating geographical influence and social correlations into PRPOIR, it can alleviate the sparsity of check-ins and bring further performance improvement. To verify the effectiveness of PRPOIR, we conduct experiments on two real-world datasets, experimental results show that PRPOIR outperforms the state-of-the-art models.

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.

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

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.

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 next point-of-interest (POI) recommendation is a hot-spot for both industry and academia, which helps users better experience the physical world. However, existing methods suffer from a severe bias towards recommending repeat POIs that have been visited by the target user before, and perform inefficiently when recommending new POIs that have not been visited by the target user yet. To overcome this issue, we delve into the next new POI recommendation and uncover the coexistence of local and global exploration patterns in users’ visits to new POIs, showing their willingness to explore not only nearby new POIs but also those distant ones. Subsequently, we develop a novel L ocal and G lobal E xploration framework (LGE) for the next new POI recommendation. In particular, LGE involves three key modules: 1) a Zone-Aware Local Exploration (ZLE) module, which encourages users to explore POIs in the local area by learning zone-aware POI representations and regularizing POI prediction with zone information; 2) an Intention-Aware Global Exploration (IGE) module, which recommends POIs that meet user intentions without distance constraints by extracting static and dynamic intentions from category information; 3) a fusion module, which contains a Mean Pooling (MP) strategy and a Weighted Pooling (WP) strategy to aggregate the outputs of local and global exploration modules for the final recommendation. Experiments carried out on real-world datasets have shown the effectiveness of LGE in recommending new POIs.

Next point-of-interest (POI) recommendation aims to predict the next destination for users. In the past, most POI recommendation models were based on the user’s historical check-in trajectory to achieve recommendations. However, when these models are trained with sparse historical trajectory data, the learned user’s sequence patterns are unstable, which is difficult to obtain good recommendations. In view of the above problem, we propose the next POI recommendation approach that combines neighbor information with location popularity to alleviate the sparsity of data. Specifically, we construct User-POI graph and POI-POI graph, and use graph neural networks (GNN) to capture neighbor information of effective users on these two graphs. In addition, considering that location popularity is influenced by different times and distances, we design a dynamic method to measure the impact of location popularity on the user’s check-in preferences. In evaluating the experimental performance of two real-world datasets, our approach outperforms several classical next POI recommendation approaches.

With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average.

The Point-of-Interest (POI) transition behaviors could hold absolute sparsity and relative sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across cities to alleviate those data sparsity and imbalance problems for next POI recommendation. Recently, pre-training over a large-scale dataset has achieved great success in many relevant fields, like computer vision and natural language processing. By devising various self-supervised objectives, pre-training models can produce more robust representations for downstream tasks. However, it is not trivial to directly adopt such existing pre-training techniques for next POI recommendation, due to thelacking of common semantic objects (users or items) across different cities. Thus in this paper, we tackle such a new research problem ofpre-training across different citiesfor next POI recommendation. Specifically, to overcome the key challenge that different cities do not share any common object, we propose a novel pre-training model namedCATUS, by transferring thecategory-leveluniversal transition knowledge over different cities. Firstly, we build two self-supervised objectives inCATUS:next category predictionandnext POI prediction, to obtain the universal transition-knowledge across different cities and POIs. Then, we design acategory-transition oriented sampleron the data level and animplicit and explicit transfer strategyon the encoder level to enhance this transfer process. At the fine-tuning stage, we propose adistance oriented samplerto better align the POI representations into the local context of each city. Extensive experiments on two large datasets consisting of four cities demonstrate the superiority of our proposedCATUSover the state-of-the-art alternatives. The code and datasets are available at https://github.com/NLPWM-WHU/CATUS.

The rapid growth of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which facilitates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, i.e., temporal patterns, geographical influence, social correlations and textual content indications. For example, user's visits to locations have temporal patterns and users are likely to visit POIs near them. In real-world LBSNs such as Instagram, users can upload photos associating with locations. Photos not only reflect users' interests but also provide informative descriptions about locations. For example, a user who posts many architecture photos is more likely to visit famous landmarks; while a user posts lots of images about food has more incentive to visit restaurants. Thus, images have potentials to improve the performance of POI recommendation. However, little work exists for POI recommendation by exploiting images. In this paper, we study the problem of enhancing POI recommendation with visual contents. In particular, we propose a new framework Visual Content Enhanced POI recommendation (VPOI), which incorporates visual contents for POI recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.

Stay points extracted from trajectories are often treated as points of interest (POI) in the data preprocessing of POI recommendations. Popularity (i.e., the number of visits) is one of the important features to distinguish the value of different POIs, especially for tourists traveling in an unfamiliar city. However, popularity could not reveal a user visits the point due to its attractiveness (e.g., views) or convenience for transport (e.g., railway station). In this paper, we introduce two properties: authority and hub, to further express the popularity of stay points. We apply a weighted HITS-based algorithm to calculate the scores of the two properties and conduct various experiments to demonstrate its effectiveness. The experimental results show the potential of the introduced properties that can be applied to various POI recommendation tasks.