iGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework

Zhang, Jia-Dong; ChowMember, Chi-Yin; Li, Yanhua

doi:10.1109/tsc.2014.2328341

Cited by 91 publications

(83 citation statements)

References 41 publications

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“…Instead of making a power law distribution assumption, Cheng et al [6] modeled the probability of a user's check-in at a location as a multi-center Gaussian model. Moreover, Zhang et al [19] argued that the geographical influence on users should be unique and personal and should not be modeled as a common distribution. In their work, kernel density estimation was used to model the geographical influence as a personalized distance distribution for each user.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2017

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Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user's check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2017

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“…Although explicit ratings for POIs are not available in LBSNs, traditional CF method can be applied into this filed by treating POIs as common items [16] via exploring user preference from user check-in frequency data which implicitly reflect user preference. Besides, a wide range of properties of LBSN have been extensively explored, such as geographical information [1,10,12,17,18], social connections [10,17], temporal information [4,8,12] and user preference order [8].…”

Section: Related Workmentioning

confidence: 99%

“…Ye et al [17] model user mobility by employing a power-law distribution (PD), and propose a collaborative POI recommendation algorithm based on geographical influence via naive Bayesian. Zhang et al [18] consider that geographical influence on user mobility should be personalized when LBSNs recommend POIs to users rather than a common distribution for all users, and they model the geographical influence via using kernel density estimation (KDE). Noulas et al [12] explore the predictive power offered by user mobility features, global mobility features and temporal features, which are finally combined in two supervised learning models.…”

Section: Related Workmentioning

confidence: 99%

Modeling User Mobility via User Psychological and Geographical Behaviors Towards Point of-Interest Recommendation

Chen

Li²,

et al. 2016

Database Systems for Advanced Applications

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Abstract. The pervasive employments of Location-based SocialNetwork call for precise and personalized Point-of-Interest (POI) recommendation to predict which places the users prefer. Modeling user mobility, as an important component of understanding user preference, plays an essential role in POI recommendation. However, existing methods mainly model user mobility through analyzing the check-in data and formulating a distribution without considering why a user checks in at a specific place from psychological perspective. In this paper, we propose a POI recommendation algorithm modeling user mobility by considering check-in data and geographical information. Specifically, with check-in data, we propose a novel probabilistic latent factor model to formulate user psychological behavior from the perspective of utility theory, which could help reveal the inner information underlying the comparative choice behaviors of users. Geographical behavior of all the historical check-ins captured by a power law distribution is then combined with probabilistic latent factor model to form the POI recommendation algorithm. Extensive evaluation experiments conducted on two real-world datasets confirm the superiority of our approach over state-of-the-art methods.

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“…Geographical influence has already been studied in several previous works [88,78,74,85,87,49] and explains why LBSN users tend to visit the POIs which are near to the venues they have already visited [29]. Such effect has been already modeled using Power law distribution [78,74,85], Multi-Center Gaussian Model [7], and the personalized Kernel Density Estimation [88].…”

Section: Geographical Influencementioning

confidence: 99%

“…Such effect has been already modeled using Power law distribution [78,74,85], Multi-Center Gaussian Model [7], and the personalized Kernel Density Estimation [88]. We have also utilized geographical influence jointly with social and multi-aspect temporal factors (Chapter 4).…”

Section: Geographical Influencementioning

confidence: 99%

Location Inference and Recommendation in Social Networks

Hosseini¹

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With the ubiquity of GPS-enabled smartphones, Location-Based Service (LBS) as a prominent product of social networks has become an essential part of our daily lives. People can easily socialize and share their check-in data (location, time, text, photo and etc.) via Location-Based Social Networks (LBSN). Through mining the check-in dataset, Point-Of-Interest(POI) recommendation systems assist users in exploring new attractive venues.The primary challenge regarding POI recommendation is to suggest a list of new interesting locations to the query user. Specifically, the excessive sparsity observed in user-location matrices is the main problem. The well known Collaborative Filtering (CF) methods are commonly used with other factors like geographical, social, context-oriented (e.g. text contents) and temporal effects to promote the effectiveness of the location recommendation systems. Despite the vital role of temporal influence, an insufficient amount of research has been devoted to considering the time factor in location recommendation. While we have an insufficient number of records regarding a user's check-in at a particular location, predicting the time of the visit seems more problematic. We have dedicated part of our research to study various aspects of the temporal influence in the recommendation.Additionally, we use Twitter textual contents to extract a set of spatial phrases associated with each region. Such an act enriches the textual contents of the local POIs. This process enhances location recommendation systems, as it facilitates textual similarity among POI tags and the tweet history of the query user.In short, we aim to address the problems involved with both aspects of Location Inference and Recommendation in social networks. Our research in this thesis has four parts. Firstly, we define a problem which merely considers a single temporal aspect to enhance the performance of a location recommendation task. Subsequently, we develop a probabilistic model which detects a user's temporal orientation based on visibility weights of POIs visited by her during weekday/weekend cycles. While this method is limited to a single temporal scale, the idea can be adapted to other time-related periodic cycles (e.g. daily home-work return trips). Secondly, we argue that the majority of existing methods merely concentrate on a limited number of temporal scales and neglect others. We propose a probabilistic generative model, named after Multi-aspect Time-related Influence (MATI) which employs the user's check-in log to detect her temporal mobility pattern under various scales (e.g. minute, hour, day and so on). It then performs recommendation using multi-aspect temporal correlations between the query user and proposed locations. Thirdly, we further study the role of the time factor in recommendation models. We define a new problem to jointly model a pair of heterogeneous time-related 2 effects (recency and the subset feature) in location recommendation. To address the challenges, we propose a generative model ...

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iGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework

Cited by 91 publications

References 41 publications

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

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

Modeling User Mobility via User Psychological and Geographical Behaviors Towards Point of-Interest Recommendation

Location Inference and Recommendation in Social Networks

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