iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
05 Nov 2013-pp 334-343
TL;DR: Experimental results show that iGSLR provides significantly superior location recommendation compared to other state-of-the-art geo-social recommendation techniques.
Abstract: With the rapidly growing location-based social networks (LBSNs), personalized geo-social recommendation becomes an important feature for LBSNs Personalized geo-social recommendation not only helps users explore new places but also makes LBSNs more prevalent to users In LBSNs, aside from user preference and social influence, geographical influence has also been intensively exploited in the process of location recommendation based on the fact that geographical proximity significantly affects users' check-in behaviors Although geographical influence on users should be personalized, current studies only model the geographical influence on all users' check-in behaviors in a universal way In this paper, we propose a new framework called iGSLR to exploit personalized social and geographical influence on location recommendation iGSLR uses a kernel density estimation approach to personalize the geographical influence on users' check-in behaviors as individual distributions rather than a universal distribution for all users Furthermore, user preference, social influence, and personalized geographical influence are integrated into a unified geo-social recommendation framework We conduct a comprehensive performance evaluation for iGSLR using two large-scale real data sets collected from Foursquare and Gowalla which are two of the most popular LBSNs Experimental results show that iGSLR provides significantly superior location recommendation compared to other state-of-the-art geo-social recommendation techniques
Citations
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24 Aug 2014
TL;DR: The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon in human mobility behavior on the LBSNs into matrixfactorization improves recommendation performance.
Abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations However, extreme sparsity of user-POI matrices creates a severe challenge To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit weighted matrix factorization for this task since it usually serves collaborative filtering with implicit feedback better Besides, researchers have recently discovered a spatial clustering phenomenon in human mobility behavior on the LBSNs, ie, individual visiting locations tend to cluster together, and also demonstrated its effectiveness in POI recommendation, thus we incorporate it into the factorization model Particularly, we augment users' and POIs' latent factors in the factorization model with activity area vectors of users and influence area vectors of POIs, respectively Based on such an augmented model, we not only capture the spatial clustering phenomenon in terms of two-dimensional kernel density estimation, but we also explain why the introduction of such a phenomenon into matrix factorization helps to deal with the challenge from matrix sparsity We then evaluate the proposed algorithm on a large-scale LBSN dataset The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon into matrix factorization improves recommendation performance
582 citations
TL;DR: A panorama of the recommender systems in location-based social networks with a balanced depth is presented, facilitating research into this important research theme.
Abstract: Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users' preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users' travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme.
520 citations
Cites background from "iGSLR: personalized geo-social loca..."
...In terms of improving the efficiency of the location recommendations [20, 107], Chow et al., propose a new recommendation algorithm that using the safe region technique to reduce the system communicational and computational overhead for the users moving on their paths....
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...Zhang and Chow [125] further explore the geographical influences in location recommendation, from the perspective of a user’s personalized travel pattern....
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...These systems can be divided into two groups by the objectives of their recommendation: 1) stand-alone location recommender systems, which Table 3 Summary of the existing recommender systems in location-based social networks Objectives Methodologies Data sources Social Location User Activity Content Link CF User Individual User Media based analysis profile locations trajectories Sandholm [83] √ √ √ Levandoski [55] √ √ √ √ Park [76] √ √ √ Horozov [45] √ √ √ Ye [111] √ √ √ Chow [20] √ √ √ Ye [112] √ √ √ Tai [94] √ √ √ Yoon [121] √ √ √ Cao [13] √ √ √ Ye [110] √ √ √ Liu [62] √ √ √ Zheng [136] √ √ √ √ Zheng [133] √ √ √ √ Li [56] √ √ √ Hung [47] √ √ √ Xiao [106] √ √ √ √ Ying [120] √ √ √ √ Scellato [87] √ √ √ Zheng [127] √ √ √ √ √ Symeonidis [75] √ √ √ √ Yin [114] √ √ √ √ provide a user with individual locations, such as restaurants or cities, that match their preferences, and 2) sequential location recommender systems, which recommend a series of locations (e.g., a popular travel route in a city) to a user based on their preferences and their constraints, such as in time and cost....
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03 Nov 2014
TL;DR: This paper proposes a novel recommendation approach, namely Instance-Region Neighborhood Matrix Factorization (IRenMF), which exploits two levels of geographical neighborhood characteristics: a) instance-level characteristics, i.e., nearest neighboring locations tend to share more similar user preferences; and b) region- level characteristics,i.e, locations in the same geographical region may share similaruser preferences.
Abstract: Geographical characteristics derived from the historical check-in data have been reported effective in improving location recommendation accuracy. However, previous studies mainly exploit geographical characteristics from a user's perspective, via modeling the geographical distribution of each individual user's check-ins. In this paper, we are interested in exploiting geographical characteristics from a location perspective, by modeling the geographical neighborhood of a location. The neighborhood is modeled at two levels: the instance-level neighborhood defined by a few nearest neighbors of the location, and the region-level neighborhood for the geographical region where the location exists. We propose a novel recommendation approach, namely Instance-Region Neighborhood Matrix Factorization (IRenMF), which exploits two levels of geographical neighborhood characteristics: a) instance-level characteristics, i.e., nearest neighboring locations tend to share more similar user preferences; and b) region-level characteristics, i.e., locations in the same geographical region may share similar user preferences. In IRenMF, the two levels of geographical characteristics are naturally incorporated into the learning of latent features of users and locations, so that IRenMF predicts users' preferences on locations more accurately. Extensive experiments on the real data collected from Gowalla, a popular LBSN, demonstrate the effectiveness and advantages of our approach.
310 citations
Cites methods from "iGSLR: personalized geo-social loca..."
...[32] developed a unified geo-social recommendation framework, namely iGSLR, in which a kernel density estimation approach was used to personalize the geographical influence on users’ check-in behaviors as individual distributions....
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04 Aug 2017
TL;DR: This work proposes to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs.
Abstract: Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.
299 citations
09 Aug 2015
TL;DR: A new POI recommendation approach called GeoSoCa is proposed through exploiting geographical correlations, social correlations and categorical correlations among users and POIs to achieve significantly superior recommendation quality compared to other state-of-the-artPOI recommendation techniques.
Abstract: Recommending users with their preferred points-of-interest (POIs), e.g., museums and restaurants, has become an important feature for location-based social networks (LBSNs), which benefits people to explore new places and businesses to discover potential customers. However, because users only check in a few POIs in an LBSN, the user-POI check-in interaction is highly sparse, which renders a big challenge for POI recommendations. To tackle this challenge, in this study we propose a new POI recommendation approach called GeoSoCa through exploiting geographical correlations, social correlations and categorical correlations among users and POIs. The geographical, social and categorical correlations can be learned from the historical check-in data of users on POIs and utilized to predict the relevance score of a user to an unvisited POI so as to make recommendations for users. First, in GeoSoCa we propose a kernel estimation method with an adaptive bandwidth to determine a personalized check-in distribution of POIs for each user that naturally models the geographical correlations between POIs. Then, GeoSoCa aggregates the check-in frequency or rating of a user's friends on a POI and models the social check-in frequency or rating as a power-law distribution to employ the social correlations between users. Further, GeoSoCa applies the bias of a user on a POI category to weigh the popularity of a POI in the corresponding category and models the weighed popularity as a power-law distribution to leverage the categorical correlations between POIs. Finally, we conduct a comprehensive performance evaluation for GeoSoCa using two large-scale real-world check-in data sets collected from Foursquare and Yelp. Experimental results show that GeoSoCa achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
290 citations
References
More filters
01 Jan 1986
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Abstract: Introduction. Survey of Existing Methods. The Kernel Method for Univariate Data. The Kernel Method for Multivariate Data. Three Important Methods. Density Estimation in Action.
15,499 citations
TL;DR: It is proved the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density.
Abstract: A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.
11,727 citations
"iGSLR: personalized geo-social loca..." refers methods in this paper
...Noth that: (1) in iGSLR the the geographical influence of locations is personalized, as presented in Section 3; (2) the model parameters of PD are obtained using maximum likelihood estimation; and (3) the centers of MGM are discovered by the mean-shift clustering algorithm [4]....
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01 Apr 2001
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Abstract: Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
8,634 citations
"iGSLR: personalized geo-social loca..." refers methods in this paper
...Memory-based methods can be further grouped into user-based CF [10] and item-based CF [19]....
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...or (ii) the item-based CF method [19] (“item” means “location” in the case of LBSNs):...
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21 Aug 2011
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
Abstract: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.
2,922 citations
"iGSLR: personalized geo-social loca..." refers background or methods in this paper
...Finally, we have conducted extensive experiments to evaluate the performance of iGSLR using two large-scale real data sets collected from Foursquare and Gowalla....
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...Recently with the emergence of LBSNs, like Foursquare, Gowalla, and Facebook places, recommending locations (i.e., POIs) for users becomes prevalent....
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...(Section 4) • We conduct extensive experiments to evaluate the performance of iGSLR using two large-scale real data sets collected from Foursquare and Gowalla....
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...However, the improvement of SCF could be considerably limited, because in general users with social friendships only share less than 10% commonly visited locations [2, 3, 24]....
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...(2) On the Gowalla data set with one-order-of-magnitude lower density, iGSLR still outperforms the other recommendation methods to a large extent, whereas PD deteriorates dramatically, even worse than SG and MGM. (3) Since it is important for LBSNs to provide good recommendation for cold-start users, iGSLR is better than other state-of-the-art geo-social recommendation techniques for LBSNs to attract new users....
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01 Aug 1999
2,686 citations