RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Safavi, Sadaf; Jalali, Mehrdad

doi:10.3390/fi13030079

Cited by 21 publications

(17 citation statements)

References 39 publications

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“…The ability to detect arrangements of data or characteristics is called pattern recognition. In other words, Pattern recognition can be categorized as a classification method based on knowledge already obtained or on statistical information mined from patterns or their representation and is implemented in the domain of computer vision for countless applications like recommendation systems, data mining, and biological imaging 28–31 …”

Section: Methodsmentioning

confidence: 99%

“…In other words, Pattern recognition can be categorized as a classification method based on knowledge already obtained or on statistical information mined from patterns or their representation and is implemented in the domain of computer vision for countless applications like recommendation systems, data mining, and biological imaging. [28][29][30][31] F I G U R E 2 A graphical demonstration of utilizing clustering method to two various time span for a user In today's pattern recognition strategies and their applications in many fields, the convolutional neural network (CNN) structures illustrate a massive breakthrough in data analyzing and processing. The CNN models principally deduce the relation between some key details, textural content and are utilized at the core of every model from data mining to the prediction of visiting new sites by people.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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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

Safavi

Jalali

2022

Concurrency and Computation

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Today, millions of active users spend a percentage of their time on location-based social networks like Yelp and Gowalla and share their rich information. They can easily learn about their friends' behaviors and where they are visiting and be influenced by their style. As a result, the existence of personalized recommendations and the investigation of meaningful features of users and Point of Interests (POIs), given the challenges of rich contents and data sparsity, is a substantial task to accurately recommend the POIs and interests of users in location-based social networks (LBSNs). This work proposes a novel pipeline of POI recommendations named DeePOF based on deep learning and the convolutional neural network. This approach only takes into consideration the influence of the most similar pattern of friendship instead of the friendship of all users. The mean-shift clustering technique is used to detect similarity. The most similar friends' spatial and temporal features are fed into our deep CNN technique. The output of several proposed layers can predict latitude and longitude and the ID of subsequent appropriate places, and then using the friendship interval of a similar pattern, the lowest distance venues are chosen. This combination method is estimated on two popular datasets of LBSNs. Experimental results demonstrate that analyzing similar friendships could make recommendations more accurate and the suggested model for recommending a sequence of top-k POIs outperforms state-of-the-art approaches.

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Section: Methodsmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

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

Safavi

Jalali

2022

Concurrency and Computation

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“…POI recommender systems can employ types of entities in LBSNs, including the following: Therefore, the existence of an environment that can integrate all these features of the entities according to their different types can be effective in improving the accuracy of the recommender system [3,6,69].…”

Section: Point-of-interest Recommender Systemsmentioning

confidence: 99%

“…The assumption is that the clients in their decisions are persuaded by the explanations and suggestions of their friends compared to other users. The importance of a friend [3] evaluates the influence of a friend while visiting the POI. Numerous studies [81][82][83][84][85][86] indicate that social relations are beneficial for the recommender systems, and the use of social factors to reinforce traditional recommendation systems has been investigated, both in memory-based methods [87,88] and in model-based techniques [89][90][91].…”

Section: • Social Influence and Importance Of Friends' Behaviormentioning

confidence: 99%

“…In recent years, employing deep-learning strategies has seen a persistent rise in artificial intelligence works such as POI recommendation, computer vision, and natural language processing where main traits can be exploited extremely and successfully [2][3][4][5][6][7][8]. Universities and industries use this technique for a wider range of applications due to its ability to solve many complex tasks while delivering acceptable outcomes because deep learning outlines a representation-learning method that is dependable for learning data representations with several uncomplicated components.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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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Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Song

et al. 2023

IEEJ Transactions Elec Engng

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Point‐of‐interest (POI) recommendation has always faced serious data sparsity problem. Most of the existing POI recommendation methods do not consider the bidirectional relationship between the user‐preference for locations and the location‐ attractiveness to users in the original dataset when solving the sparsity. For this problem, a point‐of‐interest recommendation method based on bidirectional matrix and deep belief network (POI_DBNBM) is proposed in this paper. Specially, from the perspectives of users and locations, we first construct two different matrices with bidirectional relationship by mapping the original dataset, and we use deep belief network (DBN) with strong learning ability to predict the two matrices. Then, a local–global weight optimization method is developed to weight and fuse the two result matrices obtained by DBN, so as to achieve the more precise user's rating prediction matrix for locations. The abundant experimental studies and evaluations are conducted on FourSquare dataset. Results are encouraging and better than those previously reported on the dataset. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Cited by 21 publications

References 39 publications

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

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

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

Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

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