Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

Guo, Lei; Jiang, Haoran; Wang, Xinhua; Fang-ai, Liu

doi:10.3390/info8010020

Cited by 15 publications

(12 citation statements)

References 28 publications

(28 reference statements)

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“…Semantic information from POIs has been used in a myriad of studies and applications, such as mobile POI recommendation [10,11], spatial analyses of socio-economic processes [12,13], land-use estimation from individual buildings [14,15], grid cells [16] and urban parcels [17,18], neighbourhood vibrancy description [19], semantic enrichment of streets segments [19,20], urban mobility modelling [21,22] and pedestrian navigation [23,24], to name a few. These and other studies and applications can benefit a lot from the conflation of POI semantic information dispersed across different VGI sources.…”

Section: Steps In the Matching Of Pois From Different Datasetsmentioning

confidence: 99%

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

Novack

Peters

Zipf

2018

IJGI

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Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a "naive", though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method.

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Section: Steps In the Matching Of Pois From Different Datasetsmentioning

confidence: 99%

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

Novack

Peters

Zipf

2018

IJGI

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“…On the one hand, the traditional recommendation approaches commonly obtain user preferences through ratings that he/she provides to certain items in an application or service, such as books, movies, or music [ 2 , 14 ]. On the other hand, the POI recommendation systems model the users’ visiting preferences in order to recommend POIs that the user never visited before but could be interested in [ 5 , 7 , 15 ]. Therefore, according to these definitions and the scope of the proposed Smart POI recommendations, this research is mainly focused on the related work to the POI recommendation algorithms.…”

Section: State Of the Artmentioning

confidence: 99%

“…In the same year, Zhang and Wang [ 18 ] proposed a location and time aware social collaborative retrieval model (LTSCR) for the successive POI recommendation task considering the user’s location, time, and social information simultaneously. Finally, Guo et al [ 15 ] proposed a weighted Bayesian personalized ranking model with visit frequency and distance (WBPR-FD) to give POI recommendations using user’s check-ins and geographical distance.…”

Section: State Of the Artmentioning

confidence: 99%

“…In summary, according to Table 1 , it is concluded that only one approach [ 19 ] in addition to HyRA addresses ratings, nine of them address check-ins [ 5 , 6 , 7 , 10 , 15 , 16 , 17 , 18 , 20 ]; and one addresses both ratings and check-ins [ 21 ]. Geographical influence is the factor most used than social influence with eight [ 5 , 6 , 7 , 10 , 15 , 17 , 18 , 21 ] (in addition to HyRA) and four [ 10 , 16 , 18 , 20 ] works, respectively. The POI categories—six approaches [ 5 , 16 , 17 , 19 , 20 , 21 ] (in addition to HyRA)—are also more explored than social influence.…”

Section: State Of the Artmentioning

confidence: 99%

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HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Alvarado-Uribe

Gómez-Oliva

Barrera-Animas

et al. 2018

Sensors

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Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on the Web such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.

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“…The recommender system aims to solve the problem of information overload and help users get the best choice from a variety of choices [3]. The key idea of the recommender system is to predict the rating of a group of unrated items according to user historical behavior, and then select personalized recommendation from the items with the top predicted ratings [4].…”

Section: Introductionmentioning

confidence: 99%

FSCR: A Deep Social Recommendation Model for Misleading Information

Zhang

Yang

2021

Information

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The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people’s behaviors. Misleading information refers to false information made up by some malicious marketer to create panic and seek benefits. In particular, when emergency events break out, many users may be misled by the misleading information on the Internet, which further leads them to buy things that are not in line with their actual needs. We call this kind of human activity ‘emergency consumption’, which not only fails to reflect users’ true interests but also causes the phenomenon of user preference deviation, and thus lowers the accuracy of the personal recommender system. Although traditional recommendation models have proven useful in capturing users’ general interests from user–item interaction records, learning to predict user interest accurately is still a challenging problem due to the uncertainty inherent in user behavior and the limited information provided by user–item interaction records. In addition, to deal with the misleading information, we divide user information into two types, namely explicit preference information (explicit comments or ratings) and user side information (which can show users’ real interests and will not be easily affected by misleading information), and then we create a deep social recommendation model which fuses user side information called FSCR. The FSCR model is significantly better than existing baseline models in terms of rating prediction and system robustness, especially in the face of misleading information; it can effectively identify the misleading users and complete the task of rating prediction well.

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Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

Cited by 15 publications

References 28 publications

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

FSCR: A Deep Social Recommendation Model for Misleading Information

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