Location Recommendation Algorithms Research Articles

Three-layered location recommendation algorithm using spectral clustering

Users utilize Location-Based Social Networks (LBSNs) to check into diverse venues and share their experiences through ratings and comments. However, these platforms typically feature a considerably larger number of locations than users, resulting in a challenge known as insufficient historical data or user-location matrix sparsity. This sparsity arises because not all users can check into all available locations on a given LBSN, such as Yelp. To address this challenge, this paper proposes combining Spectral Clustering with a three-layered location recommendation model to develop a recommender system named LSC, applied to Yelp datasets. LSC leverages various information, including users’ check-in data, demographics, location demographics, and users’ friendship network data, to train the recommender system and generate recommendations. Evaluation of LSC’s performance utilizes the Yelp dataset and several comparison metrics, such as accuracy, RMSE, and F1-score. The results demonstrate that our proposed algorithm delivers reliable and significant performance improvements across various evaluation metrics compared to competing algorithms.

Smart City Facility Location Recommendation Algorithm Using Multimedia Data and Improved Generative Adversarial Networks

Internet of Things, low energy consumption, and intelligent and functionally integrated urban infrastructure construction are crucial elements in the development of smart cities. Distributed generation (DG) and electric vehicle charging infrastructure play a vital role in the planning and construction of smart cities. However, the uncertainty associated with the power output of distributed generation significantly impacts the planning of distribution networks. To address this issue, this paper proposes a site-selection recommendation algorithm that leverages urban multimedia data and improved generative adversarial networks. The proposed algorithm begins by modeling the uncertainty of wind power and photovoltaic (PV) generation using an enhanced conditional generative adversarial network model. To generate multimedia datasets with time-series characteristics for wind power and PV generation scenarios, monthly multimedia data labels are incorporated into the model. These multimedia datasets, representing a wide range of scenarios, are then clustered using the K-means clustering method. Furthermore, a distributed generation planning model is established, aiming to minimize the annual integrated cost. The planning problem is efficiently solved using CPLEX, a mathematical programming solver. In the simulation experiments, the proposed scheme is compared with alternative schemes. The results demonstrate that the proposed scheme achieves a significant total cost saving of 21.95% compared to the comparison scheme. Moreover, the experimental comparison reveals that the proposed scheme exhibits higher stability. Additionally, in terms of algorithm efficiency, the proposed algorithm outperforms the other three algorithms tested in terms of the number of iterations and speed. The experimental results highlight the effectiveness of the proposed planning model in improving the economy and stability of the distribution network. Furthermore, it enhances the computational efficiency of the planning problem associated with distributed power supply and electric vehicle charging stations. The findings of this research hold substantial research significance for the site selection planning of distributed power supply.

DATA DRIVEN FRAMEWORK FOR ANALYSIS OF AIR QUALITY LANDSCAPE FOR THE CITY OF LAHORE

Abstract. Several Pakistani cities are among the world’s most polluted. In the previous three years, air pollution in Lahore has been considerably over World Health Organization guideline levels, endangering the lives of the city’s more than 11 million citizens. In this paper, we investigate the city’s capability to combat air pollution by analyzing three essential aspects: (1) Data, (2) Capacity, and (3) Public awareness. Several studies have reported the need for expansion of the current air quality monitoring network. In this work, we also provide a context-aware location recommendation algorithm for installing the new air quality stations in Lahore. Data from four publicly available reference-grade continuous air quality monitoring stations and nine low-cost air quality measuring equipment are also analyzed. Our findings show that in order to measure and mitigate the effects of air pollution in Lahore, there is an urgent need for capacity improvement (installation of reference-grade and low-cost air quality sensors) and public availability of reliable air quality data. We further assessed public awareness by conducting a survey. The questionnaire results showed huge gaps in public awareness about the harms of the air quality conditions. Lastly, we provided a few recommendations for designing data-driven policies for dealing with the current apocalyptic air quality situation in Lahore.

A robust personalized location recommendation based on ensemble learning

Recommender systems (RSs) have attracted considerable attention with the aim of optimizing location service efficiency since a large volume of information is generated by location-based social networks. Prediction accuracy is generally considered the main performance evaluation criterion, while the stability of RSs, which might be affected by uncertainty, has rarely been documented. To guarantee the robustness of RSs on the basis of two essential elements, accuracy and stability, this paper proposes an ensemble-based personalized location recommendation (EPLR) algorithm, in which several different categories of individual models are pre-trained to provide a knowledge base, and the accuracy metric in terms of F1 and the information gain (IG) are individually calculated for each user and are used as personalized weights to integrate the individual models. Notably, IG is used as an evaluation index of system stability. To demonstrate and evaluate EPLR in detail, four representative recommendation algorithms, i.e., user-based collaborative filtering (UBCF), singular value decomposition (SVD), friend-based collaborative filtering (FCF) and kernel density estimation (KDE), are selected as individual models for demonstration. Extensive experiments are conducted on two popular datasets: Brightkite and Gowalla. Additionally, three published ensemble recommendation algorithms and four individual models are implemented for comparison. The experimental results show that EPLR outperforms the other considered algorithms in terms of prediction accuracy and exhibits a promising advantage in system stability.

A Multi-Element Hybrid Location Recommendation Algorithm for Location Based Social Networks

In the environment of data explosion, how to make an effective and accurate personalized point of interest (POI) recommendation in location-based social networks (LBSNs) is a challenging and meaningful task. Fortunately, there is a lot of information that we can use. We can make recommendations by mining the rich information hidden in user check-in records. In this paper, we propose a recommend system named GFP-LORE. Specifically, we have designed a framework, which integrates various influencing factors. First, we modeled friend sign-in frequencies and POI popularity as a power-law distribution and the experiment proved that it is effective. Then, we got the influence of geographic information by modeling it according to the longitude and latitude of the user’s check-in location. After that, we sorted the historical check-in records of all users according to time and then mine an overall pattern of location transfer-order pattern. Then, we combine it with each user’s own unique location transfer record to get the possibility of the user going to the next POI. Finally, we synthesize the above four influence factors into a unified correlation probability rating and recommend a new location by this probability rating. We tested our system on the open real check-in data set, and the results of our simulation experiments show that the recommendation effect of our system is better than those algorithms used in the contrast test.

On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors

Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. We used the data of terrorist attacks in Southeast Asia from 1970 to 2016, and comprehensively considered 17 influencing factors, including socioeconomic and natural resource factors. The improved recommendation algorithm is used to build a spatial risk assessment model of terrorist attacks, and the effectiveness is tested. The model trained in this study is tested with precision, recall, and F-Measure. The results show that, when the threshold is 0.4, the precision is as high as 88%, and the F-Measure is the highest. We assess the spatial risk of the terrorist attacks in Southeast Asia through experiments. It can be seen that the southernmost part of the Indochina peninsula and the Philippines are high-risk areas and that the medium-risk and high-risk areas are mainly distributed in the coastal areas. Therefore, future anti-terrorism measures should pay more attention to these areas.

STSCR: Exploring spatial-temporal sequential influence and social information for location recommendation

Abstract One remarkable progress in spatial data analysis field lies on location-based social networks (LBSNs), where location recommendation has become an important way to help people locate the interesting places. When providing the location recommendations for users, spatial-temporal sequence of locations on users check-in behaviors have been intensively studied based on the fact that the human movement usually exhibits a pattern of spatial-temporal sequence. However, despite that some works have been proposed to focus on the spatial-temporal correlation among the sequences of users checked-in locations, they still suffer to the following their limitations: (1) these works are unable to handle time intervals between nearby check-in behaviors properly in modeling sequential data. In fact, POIs also have different probabilities of being checked in different time periods; (2) these works fail to employ the social relations adequately to improve final recommendation results especially when the check-in data is sparse; (3) these works are essentially based on the point-wise theory that aims to regress a real-valued score, whereas few work attempt to build a ranking-based estimator for recommendation. To solve the problems mentioned above, a new location recommendation model called Spatial-Temporal aware Social Collaborative Ranking(STSCR) model is proposed to explore the impact of time, spatial-temporal sequential influence and social influence. In particular, the proposed model is built upon a unified tensor factorization framework in which the ne-grained modeling of user-location, user-friend, friend-location, location-time, and location-location interactions. Then, the Bayesian personalized ranking technique is used to optimized the loss function of unified tensor factorization and fit the partial order of locations. As is shown in the experimental results derived from real datasets, our proposed STSCR achieves better recommendation performances than other state-of-the-art location recommendation algorithms.

Location Recommendation of Digital Signage Based on Multi-Source Information Fusion

With the increasing amount of digital signage and the complexity of digital signage services, the problem of introducing precise location recommendation methods for digital signage should be solved by digital signage enterprises. This research aims to provide a sustainable location recommendation model that integrates the spatial characteristics of geographic locations and multi-source feature data to recommend locations for digital signage. We used the outdoor commercial digital signage within the Sixth Ring Road area in Beijing as an example and combined it with economic census, population census, average house prices, social network check-in data, and the centrality of traffic networks that have an impact on the sustainable development of the regional economy as research data. The result shows that the proposed method has higher precision and recall in location recommendation, which indicates that this method has a better recommendation effect. It can further improve the recommendation quality and the deployment of digital signage. By this method, we can optimize resource allocation and make the economics and resources sustainable. The digital signage recommendation results of the Beijing City Sixth Ring Road indicated that the areas suitable for digital signage were primarily distributed in Wangfujing, Financial Street, Beijing West Railway Station, and tourist attractions in the northwest direction of the Fifth Ring Road. The research of this paper not only provides a reference for the integration of geographical features and their related elements data in a location recommendation algorithm but also effectively improves the science of digital signage layout, prompting advertising efforts to advance precision, personalization, low carbonization, and sustainable development.

PatchSwapper: A novel real-time single-image editing technique by region-swapping

While many image composition and synthesis techniques have been proposed, neither existing work nor professional image editing softwares such as Adobe Photoshop© provide a program explicitly for the task of realistically swapping regions within a single image. In this paper, we present an easy-to-use image-editing tool explicitly for that purpose, named as PatchSwapper. Users can simply determine the centre and radius of a pair of candidate regions or specify arbitrary borders by sketches; then, the swapping step is automatically and optimally executed. A graph-based approach is designed to find the optimal borders of two irregular regions to avoid generating visible seams. We use this approach to handle both non-transformed and transformed patches. For non-transformed cases, we not only achieve realtime performance with CPU multi-thread implementation, we also provide a location recommendation algorithm to help users find the appropriate exchangeable areas. For transformed patches, our method searches for the optimal transformation to generate interesting appearance changes on both the source and target locations. A Poisson color blending is performed onto the stitched patches. Overall the proposed approach is not only suitable for swapping image objects of any shapes but also for some other applications, e.g., image completion and composition.The experiments demonstrate that common images often contain potentially exchangeable areas and that large content variations can be obtained by simply swapping their locations.

Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately.

Similarity-based probabilistic category-based location recommendation utilizing temporal and geographical influence

Location recommendation on location-based social networks, which is a rapidly growing research topic, suggests recommendations for unvisited locations to their users. This recommendation service is based on users’ visit histories and location-related information, such as location categories. Finding similarities in users’ behaviors can help these networks make better recommendations. In this paper, we propose two similarity-based Probabilistic Category-based Location Recommendation (sPCLR) algorithms that recommend locations to users at a given time of the day by utilizing category information: sPCLR-DTW and sPCLR-BCC. Both algorithms utilize temporal and spatial components. The temporal component utilizes the temporal influence of similar users’ check-in behaviors by representing users’ periodic check-in behavior at different location categories as temporal curves. The similarity between users’ periodic check-in behavior is calculated based on the difference between temporal curves. In sPCLR-DTW, the traditional dynamic time warping method (DTW) is used to measure the distance between temporal curves. In order to improve the time efficiency of the recommendation process, a novel sequence-matching technique—best curve coupling (BCC)—is proposed and utilized in sPCLR-BCC. The spatial component utilizes the geographical influence of locations and filters out those locations that are not of interest to the user. The performance of the sPCLR-DTW and sPCLR-BCC algorithms are studied and compared to two existing location recommendation algorithms on a real-world dataset. Experimental results show that the sPCLR-DTW performs better than all other recommendation algorithms, whereas sPCLR-BCC can provide comparable recommendations more quickly, making it ideal for online applications.