Point Of Interest Recommendation System Research Articles

SQPMF: successive point of interest recommendation system based on probability matrix factorization

SQPMF: successive point of interest recommendation system based on probability matrix factorization

An ontology-based web decision support system to find entertainment points of interest in an urban area

ABSTRACT In recent years, decision support systems (DSSs) have successfully deployed ontologies in their architecture. The result of such a use is information systems that assist users and organizations in semi-structured decision-making activities. Visitors from throughout Iran travel to different cities and regions every year, and they need help making their choices. Some of these tourists are unable to visit the beautiful areas of the destination city due to a lack of awareness. In this study, we design an ontology-based spatial DSS to find entertainment and tourism centers in Arak, Iran. The objective is to provide users with recommendations appropriate for the location, time, age group, type of activity, and other factors. In this model, the demands and concerns of tourists have been managed by creating a domain Web Ontology Language (OWL) for entertainment centers as a knowledge base in the Protégé environment. The developed web-based DSS operates on a client-server architecture using technologies such as Werkzeug and Flask. As a result, it makes it possible to ontology reasoning based on the HermiT engine to choose the right center and conduct a semantic search on classes related to the appropriate point of interest. The main distinction between the proposed methodology and the previous studies on spatial DSS is that criteria are object properties in an ontology. Therefore, decision support relies on real-time reasoning rather than transforming criteria into geospatial layers. The evaluation results confirmed efficient interaction with this system, purposeful information retrieval, and rapid decision-making process. The results also indicated that searching for a POI (point of interest) in the study area using the developed system is at least 30% more successful than a search engine or social media. Moreover, to overcome the cold start problem, the proposed technique might be utilized in conjunction with the POI recommender systems.

Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data

Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system.

An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

With the rapid development of point-of-interest (POI) recommendation services, how to utilize the multiple types of users’ information safely and effectively for a better recommendation is challenging. To solve the problems of imperfect privacy-preserving mechanism and insufficient response-ability to complex contexts, this paper proposes a hybrid POI recommendation model based on local differential privacy (LDP). Firstly, we introduce randomized response techniques k-RR and RAPPOR to disturb users’ ratings and social relationships, respectively and propose a virtual check-in time generation method to deal with the issue of missing check-in time after disturbance. Secondly, for simultaneously combining multiple types of information, we construct a hybrid model containing three sub-models. Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services.

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work.

The role of context fusion on accuracy, beyond-accuracy, and fairness of point-of-interest recommendation systems

Point-of-interest (POI) recommendation is an essential service to location-based social networks (LBSNs), benefiting both users providing them the chance to explore new locations and businesses by discovering new potential customers. These systems learn the preferences of users and their mobility patterns to generate relevant POI recommendations. Previous studies have shown that incorporating contextual information such as geographical, temporal, social, and categorical substantially improves the quality of POI recommendations. However, fewer works have studied in-depth the multi-aspect benefits of context fusion on POI recommendation, in particular on beyond-accuracy, fairness, and interpretability of recommendations. In this work, we propose a linear regression-based fusion of POI contexts that effectively finds the best combination of contexts for each (i) user, or (ii) group of users from their historical interactions. The results of large-scale experiments on two popular datasets Gowalla and Yelp reveal several interesting findings. First, the proposed approach does not present significant loss in accuracy and unfairness of popularity bias as with classical collaborative baselines, and yet improves the beyond-accuracy of recommendation compared with existing context-aware (CA) approaches using heuristic context fusions; for instance, the proposed approach improves the accuracy and beyond-accuracy compare to best baseline model by 25% and 30%, respectively. Second, our proposed approach is interpretable, allowing to explain to the user why she has been recommended specific POIs, based on the learned context weights from user past check-ins; for example, if you are in Rome and our method recommends you a historical place like ‘Colosseum’, it can also provide an explanation why this item is recommended to you based on your personal preference on context (e.g., you were recommended to visit ‘Colosseum’ because in the past your visited historical places). Third, by analyzing the fairness of recommendation with respect to users (based on their activity levels) and items (based on the popularity of items), we found that a model which is recommend fairly on one dataset can recommend unfair on another dataset.Overall, our study suggests that appropriate context fusion is an essential element of an accurate, fair, and transparent POI recommendation system. We highlight that while we have tested the efficacy of our context-fusion methods on two popular CA recommendation models in the POI domain, namely GeoSoCa and LORE, our system can be flexibly utilized to extend the capability of other CA algorithms.

Tag embedding based personalized point of interest recommendation system

E-tourism websites such as Foursquare, Tripadvisor, Yelp etc. allow users to rate the preferences for the places they have visited. Along with ratings, the services allow users to provide reviews on social media platforms. As the use of hashtags has been popular in social media, the users may also provide hashtag-like tags to express their opinion regarding some places. In this article, we propose an embedding based venue recommendation framework that represents Point Of Interest (POI) based on tag embedding and models the users (user profile) based on the POIs rated by them. We rank a set of candidate POIs to be recommended to the user based on the cosine similarity between respective user profile and the embedded representation of POIs. Experiments on TREC Contextual Suggestion data empirically confirm the effectiveness of the proposed model. We achieve significant improvement over PK-Boosting and CS-L2Rank, two state-of-the-art baseline methods. The proposed methods improve NDCG@5 by 12.8%, P@5 by 4.4%, and MRR by 7.8% over CS-L2Rank. The proposed methods also minimize the risk of privacy leakage. To verify the overall robustness of the models, we tune the model parameters by discrete optimization over different measures (such as AP, NDCG, MRR, recall, etc.). The experiments have shown that the proposed methods are overall superior than the baseline models.

Research on location and similar comments in point-of-interest recommendation system for users

By combining deep learning algorithms and potential probabilistic topic models, this paper proposes a new modeling strategy for individual users and constructs an effective point of interest recommendation system. First, Doc2vec is used to vectorize the user’s comments, and the Bow model is used to match the similarity of the points of interest, and then the potential probability model is used to cluster the positions of the points of interest. Finally, relying on the user’s historical comment data as a basis, the Top-K ranking recommendation scheme is obtained. The experimental results show that for large-scale location-based social network data sets, the algorithm in this paper has an ideal final effect on users’ personalized recommendations. The recommendation model for user location information and similar comment points of interest proposed in this paper is more subjectively and objectively in line with user behavior needs. On the premise of ensuring the real-time performance of recommendation, the model proposed in this paper effectively improves the accuracy of recommendation compared with traditional point-of-interest recommendation algorithms.

Exploring Temporal and Spatial Features for Next POI Recommendation in LBSNs

With the increasing popularity of Location-Based Social Networks (LBSNs), a significant volume of check-in data of users has been generated. Such massive data brings difficulties for the users to efficiently retrieve their desired point-of-interest (POI). As a result, POI recommendation systems have received extensive attention from academia and industry. Currently, most existing POI recommendation approaches only provide users with a fixed set of recommended POIs based on the historical check-in records of the users, and cannot achieve flexible and feasible recommendations according to different spatial and temporal situations of the users. In this paper, we propose a next POI recommendation model that will predict POIs to be visited by users in the next few hours according to their historical check-in data and current contextual information (such as the current time and locations of the users). In our model, we propose a unified approach to calculate context-aware similarities between different users by investigating the influences of both temporal and spatial features for the users. We also propose an approach to dynamically generate different POI recommendation lists for a particular user according to different current context information of the user. Compared with the state-of-the-art POI recommendation approaches, the experimental results demonstrate that our system achieves much better performance.

Successive Point-of-Interest Recommendation With Local Differential Privacy

A point-of-interest (POI) recommendation system performs an important role in location-based services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations. The existing POI recommendation systems require raw check-in history of users, which might cause location privacy violations. Although there have been several matrix factorization (MF) based privacy-preserving recommendation systems, they can only focus on user-POI relationships without considering the human movements in check-in history. To tackle this problem, we design a successive POI recommendation framework with local differential privacy, named SPIREL. SPIREL uses two types of information derived from the check-in history as input for the factorization: a transition pattern between two POIs and the visit counts of POIs. We propose a novel objective function for learning the user-POI and POI-POI relationships simultaneously. We further integrate local differential privacy mechanisms in our proposed framework to prevent potential location privacy breaches. Experiments using four public datasets demonstrate that SPIREL achieves better POI recommendation quality while accomplishing stronger privacy protection.

Point of interest recommendations based on the anchoring effect in location-based social network services

A point of interest (POI) recommender system (RS) is one of the representative research areas based on the location-based social network (LBSN). Most POI RS studies utilized various implicit information or social information to improve recommendation accuracy. However, majority of these studies overlooked the importance of users’ initial check-in information. Users are affected by their first input data in online services, and this phenomenon is called the anchoring effect. In POI RSs, few studies have analyzed the association with the anchoring effect while other RS domains already verified this effect. In particular, a research area, including POI RS, that focuses on the importance of the initial input does not exist. In this paper, we propose a latent Dirichlet allocation (LDA) model based on the anchoring effect for POI RS. This model emphasizes the importance of initial check-in data and is called the anchor-LDA. Experimental results showed that the anchor-LDA outperformed existing LDA-based POI recommender algorithms. Furthermore, we validated the importance of initial check-in information on the LBSN.

Pattern-based dual learning for point-of-interest (POI) recommendation

PurposeTourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.Design/methodology/approachThe authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.FindingsThe authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.Originality/valueThis study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

A Learning-Based POI Recommendation With Spatiotemporal Context Awareness.

Due to the great advances in mobility techniques, an increasing number of point-of-interest (POI)-related services have emerged, which could help users to navigate or predict POIs that may be interesting. Obviously, predicting POIs is a challenging task, mainly because of the complicated sequential transition regularities, and the heterogeneity and sparsity of the collected trajectory data. Most prior studies on successive POI recommendation mainly focused on modeling the correlation among POIs based on users' check-in data. However, given a user's check-in sequence, generally, the relationship between two consecutive POIs is usually both time and distance subtle. In this article, we propose a novel POI recommendation system to capture and learn the complicated sequential transitions by incorporating time and distance irregularity. In addition, we propose a feasible way to dynamically weight the decay values into the model learning process. The learned awareness weights offer an easy-to-interpret way to translate how much each context is emphasized in the prediction process. The performance evaluations are conducted on real mobility datasets to demonstrate the effectiveness and practicability of the POI recommendations. The experimental results show that the proposed methods significantly outperform the state-of-the-art models in all metrics.

A novel next new point-of-interest recommendation system based on simulated user travel decision-making process

Abstract POI(point of interest) recommendation systems have been widely investigated in recent years. Currently, most POI recommendation systems only recommend POIs that may be visited by users in the future, and rarely consider next new POI recommendation based on the current time and the current location of a particular user. In fact, next new POI recommendation problem is more challenging for the reason that multiple factors associated with both POIs and users need to be comprehensively incorporated in a unified recommendation system. In this paper, we design a novel and effective next new POI recommendation system. Our system simulates a user’s travel decision-making process by weighing two important factors that affect a user’s travel decision: preference factors and geographic factors. First, we use tensor to model user’s check-in history and dynamically predict user preferences. Then, in order to characterize the influence of geographic factor on individual users, we designed a personalized user similarity calculation method and fitted curves for the target user to reflect the relationship between travel distance and travel probability. Finally, a recommendation list is generated by combining the effects of these two factors on a particular user. Compared with the state-of-the-art POI recommendation approach, the experimental results demonstrate that our system achieves much better performance.

Real-time event embedding for POI recommendation

Location-based social networks (LBSNs) allow users to check-in and share daily lives with others. We have witnessed very rapid development of LBSNs in recent years. Point-of-Interest (POI) recommendation is one of the core services in LBSNs. In this study, we propose a real-time POI embedding model. Instead of capturing intrinsic information, the proposed approach is able to mine real-time information of places and learn the latent representations according to the corresponding geo-tagged posts. On one hand, we employ a Convolutional Neural Networks (CNN) to mine textual information of POIs and learn their intrinsic representation. On the other hand, a multimodal embedding model of location, time and text is applied to keep monitoring posts on POIs and extracts a set of features for representing events or burst information that may attract users. Furthermore, we combine real-time POI embedding with matrix factorization method and propose a more comprehensive POI recommendation algorithm. To verify the effectiveness of our proposed method, we conduct experiments on Twitter dataset with geo-tagged tweets in NYC. Experimental results show that POI recommendation system with taking real-time event into consideration can strongly improve the performance than the one without.

Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation

In recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot of attention. Personalized POI recommendation is a significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding environment and greatly benefits the business in real life. Although a personalized POI recommendation system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model human mobility and the difficulty to address data sparsity. Moreover, geographical influence on users should be personalized, but current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we design a novel and effective personalized POI recommendation system. First, our system mines the target user's active area based on his or her check-in history, and designs a personalized user spatial similarity calculation method based on the target user's active area. Secondly, our system takes into account three features of the human mobility pattern: spatial, temporal, and sequential properties. Furthermore, our system designs a novel personalized user mobility pattern similarity calculation method based on the features of human mobility pattern. Finally, a recommendation list is generated based on the idea of collaborative filtering. Compared with the state-of-the-art POI recommendation approaches, the experimental results demonstrate that our system achieves much better performance.

A Point-of-Interest Recommender System Based on Factorization Machines

With the rapid development of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation which can benefit both users and service providers has attracted much academic and industrial interest. Although various aspects of users' behavior data have been utilized in POI recommendation, challenges such as data sparsity and how to accurately model users' preferences exist. In this paper, we propose a model named as Geo-STFM which exploits social, temporal and geographical information for POI recommendation based on factorization machines. Specifically, we first give a precise analysis of the self-similar and other-similar characteristics in users' behaviors, and accordingly propose an improved factorization machines (FM) model with social and temporal regularization terms. Then, geographical information is fused into the model to build a POI recommender system. The experimental results on two real-world data sets show that our proposed method achieves remarkable improvements compared to the state-of-the-art POI recommendation techniques.

Investigating the utility of the weather context for point of interest recommendations

Point of interest (POI) recommender systems for location-based social networks, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated to improving recommendation services in such systems by integrating different features (e.g., time or geographic location) that are assumed to have an impact on people’s choices for POIs. Yet, little effort has been made to incorporate or even understand the impact of weather on user decisions regarding certain POIs. In this paper, we contribute to this area of research by presenting the novel results of a study that aims to recommend POIs based on weather data. To this end, we have expanded the state-of-the-art Rank-GeoFM POI recommender algorithm to include additional weather-related features such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly improves the recommendation accuracy in comparison to the original method, but also outperforms its time-based variant. Furthermore, we investigate the magnitude of the impact of each feature on the recommendation quality. Our research clearly shows the need to study weather context in more detail in light of POI recommendation systems. This study is relevant for researchers working on recommender systems in general, but in particular for researchers and system engineers working on POI recommender systems in the tourism domain.

An experimental evaluation of point-of-interest recommendation in location-based social networks

Point-of-interest (POI) recommendation is an important service to Location-Based Social Networks (LBSNs) that can benefit both users and businesses. In recent years, a number of POI recommender systems have been proposed, but there is still a lack of systematical comparison thereof. In this paper, we provide an all-around evaluation of 12 state-of-the-art POI recommendation models. From the evaluation, we obtain several important findings, based on which we can better understand and utilize POI recommendation models in various scenarios. We anticipate this work to provide readers with an overall picture of the cutting-edge research on POI recommendation.

A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS

The popularity of location-based social networks (LBSN) provides us with a new perspective for understanding people's travel behaviours and enables a lot of location-based services, such as point of interest (POI) recommendation. However, personalized POI recommendation is very challenging, as the user-location matrix is very sparse for traditional collaborative filtering (CF)-based POI recommendation approaches. The problem becomes even more challenging when people travel to a new city. In addition, temporal influence plays an important role in POI recommendation, for most users tend to visit different kinds of POIs at different time in a day, e.g., visiting a food-related POI at noon and visiting a nightlife spot at night. To the end, we propose a novel POI recommendation system, which consists of two components: context-aware tensor decomposition (CTD) for user preferences modelling and weighted HITS (Hypertext Induced Topic Search)-based POI rating (WHBPR). We model user preferences with a three-dimension tensor (user-category-time). Supplementing the missing entries of the tensor through CTD with the aid of other three matrices, we recover user preferences of different time slots. WHBPR incorporates the impacts of user preferences and social opinions on POI rating. We evaluated our method using the real Foursquare datasets, verifying the advantages of our method beyond other baselines.