A General Geographical Probabilistic Factor Model for Point of Interest Recommendation

Liu, Bin; Xiong, Hui; Papadimitriou, Spiros; Fu, Yanjie; Yao, Zijun

doi:10.1109/tkde.2014.2362525

Cited by 175 publications

(83 citation statements)

References 33 publications

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“…Gao et al [8] considered a joint model of geo-social correlations for personalised POI recommendation, where the probability of a user checking into a new POI is described as a function of correlations between user's friends and nonfriends close to and distant from a region of interest. Liu et al [9] approached the problem of POI recommendations by proposing a geographic probabilistic factor model that combines the modelling of geographic preference and user mobility. Geographic influence is captured through the identification of latent regions of activity for all users of the LBSN reflecting activity areas for the entire population and mapping the individual user mobility over those regions.…”

Section: Related Workmentioning

confidence: 99%

Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

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Knowledge of users' visits to places is one of the keys to understanding their interest in places. Usercontributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users' association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets.

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Section: Related Workmentioning

confidence: 99%

Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

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“…Due to the exponential growth of mobile e-commerce, large volume of contextual information is available, which enables researchers to study the problem of personalized context-aware recommendation [13,21,24]. Shabib & Krogstie (2011) [21] proposed a step-by-step approach to assessing the context-aware preferences, which consists of four phases: product classification, interest matrix formation, clustering similar users and making recommendation.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, they modeled the user-locationactivity relations in a tensor and designed the algorithm based on regularized tensor and matrix decomposition. Liu et al (2015) [13] considered users' check-in behavior in mobile devices to provide personalized recommendations of places, which integrated the effect of geographical factor and location based social network factor. Different from previous research, our study aims to formalize consumers' search behaviors as (when, where, what) patterns through a probabilistic generative model, which has better explanation ability and can be used to design appropriate recommendation strategies.…”

Section: Related Workmentioning

confidence: 99%

“…The size of recommendation set K ranged from 5 to 15, that is, K ∈ {5, 6,7,8,9,10,11,12,13,14 where S is the collection of records in test set and AP (s) @K can be computed by:…”

Section: (2) Evaluation Metricsmentioning

confidence: 99%

“…Usually, the clickstream data provide information about the sequence of pages or the path viewed by the consumers as they navigate websites [18]. With the prevalence of smart mobile devices [6], the consumers' clickstream data have been enriched with various contextual information [25], such as geographical information, which poses significant new opportunities as well as challenges [13]. Some data mining techniques have been employed to extract consumers' context-aware preferences [12,20].…”

Section: Introductionmentioning

confidence: 99%

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Kacprzyk

et al. 2016

Advances in Intelligent Systems and Computing

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Search activity is an essential part for gathering useful information and supporting decision making. With the exponential growth of mobile e-commerce, consumers often search for products and services that are closely relevant to the current context such as location and time. This paper studies the search behaviors of mobile consumers, which reflect their customized purchase intentions. In light of machine learning, a probabilistic generative model is proposed to discover underlying search patterns, i.e., when to search,where to search and in what category. Furthermore, the predicting power of the proposed model is validated on the dataset released by Alibaba, the biggest e-commerce platform in the world. Experimental results show the advantages of the proposed model over the classical content-based methods, and also illustrate the effectiveness of integrating contextual factors into modeling consumers search patterns.

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Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behavior

Elangovan

Subramaniyaswamy

Varadarajan

et al. 2020

Concurrency and Computation

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Location recommending frameworks plays a very significant role in suggesting the users with new places to visit especially when users are visiting unfamiliar areas. Most of the existing recommender systems do not consider the fact that different users have different behavior while checking in. Some approaches do not consider the essential factors while providing recommendations. These systems lack adaptability and hence they provide poor recommendations. An adaptive approach to provide users with a personalized recommendation has been proposed in this paper. We have considered three features namely, user activeness feature, user similarity feature, and the spatial feature. In addition to this, we have also considered the location popularity for a given timeslot. We have divided the users into inactive and active based on the degree of activeness on social networks using fuzzy c‐means clustering. We have provided two strategies based on the activeness of the user. A two‐dimensional Gaussian kernel density estimation strategy is used for the active user. A one‐dimensional power‐law function strategy is used for inactive users. Moreover, we have integrated the time‐based popularity of the location and probability estimation based on the similarity between the users. To evaluate the proposed model, we have used a large‐scale Foursquare dataset.

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A General Geographical Probabilistic Factor Model for Point of Interest Recommendation

Cited by 175 publications

References 33 publications

Spatio-semantic user profiles in location-based social networks

Spatio-semantic user profiles in location-based social networks

Flexible Query Answering Systems 2015

Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behavior

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