DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Safavi, Sadaf; Jalali, Mehrdad

doi:10.1002/cpe.6981

Cited by 21 publications

(16 citation statements)

References 46 publications

(91 reference statements)

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“…DeePOF [ 64 ]: This pipeline investigated the impact of the most analogous pattern of fellowship rather than using the pattern of the fellowship of all users. In order to detect the similarity, the mean-shift clustering strategy was employed.…”

Section: Methodsmentioning

confidence: 99%

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

et al. 2023

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In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern’s friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation.

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Section: Methodsmentioning

confidence: 99%

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

et al. 2023

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“…Moreover, recent works [34,35] leverage the multi-context via the hybrid recommendation model to generate the recommendation POIs for users. Despite effectiveness, existing hybrid-based POI recommendation methods ignore the interactions of different contextual information, which may weaken the model's performance.…”

Section: Other Poi Recommendation Methodsmentioning

confidence: 99%

Muti-channel graph attention networks for POI recommendation

Jin

Huang

2023

IFS

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This paper focuses on the task of Point-of-interest (POI) recommendation whose goal is to generate a list of POIs for a target user based on his or her history check-in records. Different from the traditional recommendation tasks (e.g., movie recommendation), there are many factors, like temporal factor and geographical factor, which make a great influence on user preference. Though existing POI recommendation methods tend to model the user preference from temporal factor, geographical factor or social factor, they fail to model these factors into a jointly model, leading to learn the suboptimal user preference. To tackle this issue, we propose a Muti-channel Graph Attention Network (MGAN) for POI recommendation which learns the user preference from multiple aspects in a unify model. Specifically, MGAN first constructs several graphs with corresponding contextual features to capture the user preference from temporal, geographical, semantic and social aspects. Then MGAN leverages the graph attention networks to learn the representations of POIs from these graphs. Finally, MGAN estimates the user preference from the history check-in records and other similar users via the learned POI representations. We conduct extensive experiments on real-world datasets. And the results indicate that our proposed MGAN outperforms mainstream POI recommendation methods.

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“…Today, in pattern recognition methods and their apps, convolution neural network techniques are a great success in data analysis. Convolution neural network architecture mainly uses the relationship between some features or structural content and is at the center of all techniques from Data Mining to predicting users visiting new POIs, recommender systems, and biological imaging [144][145][146][147][148][149].…”

Section: Poi Recommendation System Based On the Convolutional Neural ...mentioning

confidence: 99%

“…With the location service of this software, the POI status of users can be checked according to their activity time. [197] We suggest a new pipeline named DeePOF [148] regarding POI recommendations and deep learning. The purpose of this technique is to gain the proper top-K pointof-interest sequence per customer.…”

Section: Brightkitementioning

confidence: 99%

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

2022

Self Cite

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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.

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DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Cited by 21 publications

References 46 publications

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

Muti-channel graph attention networks for POI recommendation

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

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