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Proceedings Article•

Fused matrix factorization with geographical and social influence in location-based social networks

Chen Cheng1, Haiqin Yang1, Irwin King1, Michael R. Lyu1•
The Chinese University of Hong Kong1
22 Jul 2012-Vol. 26, Iss: 1, pp 17-23
TL;DR: This paper is the first to fuse MF with geographical and social influence for POI recommendation in LBSNs via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM) and fuse the geographical influence into a generalized matrix factorization framework.
Abstract: Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc, have attracted millions of users to share their social friendship and their locations via check-ins The available check-in information makes it possible to mine users' preference on locations and to provide favorite recommendations Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services To solve this task, matrix factorization is a promising tool due to its success in recommender systems However, previously proposed matrix factorization (MF) methods do not explore geographical influence, eg, multi-center check-in property, which yields suboptimal solutions for the recommendation In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs We first capture the geographical influence via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM) Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework Our solution to POI recommendation is efficient and scales linearly with the number of observations Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly

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Citations
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Proceedings Article•DOI•
Time-aware point-of-interest recommendation

[...]

Quan Yuan1, Gao Cong1, Zongyang Ma1, Aixin Sun1, Nadia Magnenat Thalmann1 •
Nanyang Technological University1
28 Jul 2013
TL;DR: This paper defines a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day, and develops a collaborative recommendation model that is able to incorporate temporal information.
Abstract: The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.

731 citations

Cites background from "Fused matrix factorization with geo..."

  • ...There exists other work that incorporates social link information into POI recommendations, such as the probabilistic generative model-based method [21], and matrix factorization-based method [4]....

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  • ...The focus of [4, 21] is to explore social link information for POI recommendations and their problem setting is different from ours....

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Proceedings Article•
Predicting the next location: a recurrent model with spatial and temporal contexts

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Qiang Liu1, Shu Wu1, Liang Wang1, Tieniu Tan1•
Chinese Academy of Sciences1
12 Feb 2016
TL;DR: RNN is extended and a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN) is proposed, which can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transitions for different geographical distances.
Abstract: Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial contextual information is collected in systems, and the location prediction problem becomes crucial and feasible. Some works have been proposed to address this problem, but they all have their limitations. Factorizing Personalized Markov Chain (FPMC) is constructed based on a strong independence assumption among different factors, which limits its performance. Tensor Factorization (TF) faces the cold start problem in predicting future actions. Recurrent Neural Networks (RNN) model shows promising performance comparing with PFMC and TF, but all these methods have problem in modeling continuous time interval and geographical distance. In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). ST-RNN can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transition matrices for different geographical distances. Experimental results show that the proposed ST-RNN model yields significant improvements over the competitive compared methods on two typical datasets, i.e., Global Terrorism Database (GTD) and Gowalla dataset.

687 citations

Cites methods from "Fused matrix factorization with geo..."

  • ...2011) or the multi-center gaussian model (Cheng et al. 2012)....

    [...]

  • ...And for spatial information, the distance between locations is calculated and the prediction is made based on power law distribution (Ye et al. 2011) or the multi-center gaussian model (Cheng et al. 2012)....

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Proceedings Article•DOI•
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation

[...]

Defu Lian1, Cong Zhao1, Xing Xie2, Guangzhong Sun1, Enhong Chen1, Yong Rui2 •
University of Science and Technology of China1, Microsoft2
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

Cites background or methods from "Fused matrix factorization with geo..."

  • ...Concentrating on modeling the distance distribution may ignore the multi-center characteristics of individual visiting locations according to [2]....

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  • ...This algorithm has been exploited in [2, 12] for POI recommendation....

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  • ...cal information to assist POI recommendation [24, 2, 12, 26] by modeling the well-known spatial clustering phenomenon, these approaches are almost independent of the procedure for collaborative filtering, particularly, matrix factorization....

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  • ...To avoid the cost in computing the distance between paired locations, in [2, 12], the authors modeled the spatial clustering phenomenon in terms of geo-clustering and tried to estimate individual spatial distribution....

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  • ..., B-NMF ) improves compared to MF-Freq since it can model the skewness of visit frequency [2, 12]....

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Journal Article•DOI•
Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs

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Dingqi Yang1, Daqing Zhang1, Vincent W. Zheng2, Zhiyong Yu3•
Institut Mines-Télécom1, Agency for Science, Technology and Research2, Fuzhou University3
01 Jan 2015
TL;DR: A STAP model is proposed that first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference, and a context-aware fusion framework is put forward to combine the temporal and spatial activity preference models for preferences inference.
Abstract: With the recent surge of location based social networks (LBSNs), activity data of millions of users has become attainable. This data contains not only spatial and temporal stamps of user activity, but also its semantic information. LBSNs can help to understand mobile users’ spatial temporal activity preference (STAP), which can enable a wide range of ubiquitous applications, such as personalized context-aware location recommendation and group-oriented advertisement. However, modeling such user-specific STAP needs to tackle high-dimensional data, i.e., user-location-time-activity quadruples, which is complicated and usually suffers from a data sparsity problem. In order to address this problem, we propose a STAP model. It first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference. In order to characterize the impact of spatial features on user activity preference, we propose the notion of personal functional region and related parameters to model and infer user spatial activity preference. In order to model the user temporal activity preference with sparse user activity data in LBSNs, we propose to exploit the temporal activity similarity among different users and apply nonnegative tensor factorization to collaboratively infer temporal activity preference. Finally, we put forward a context-aware fusion framework to combine the spatial and temporal activity preference models for preference inference. We evaluate our proposed approach on three real-world datasets collected from New York and Tokyo, and show that our STAP model consistently outperforms the baseline approaches in various settings.

548 citations

Cites background from "Fused matrix factorization with geo..."

  • ...Some previous works [12], [25], [26] have studied the probability of location visiting w....

    [...]

Proceedings Article•
Where you like to go next: successive point-of-interest recommendation

[...]

Chen Cheng1, Haiqin Yang1, Michael R. Lyu1, Irwin King1•
The Chinese University of Hong Kong1
03 Aug 2013
TL;DR: This paper proposes a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions in the check-in sequence, and utilizes the information of localized regions to boost recommendation.
Abstract: Personalized point-of-interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help provide better user experience as well as enable third-party services, e.g., launching advertisements. To provide a good recommendation, various research has been conducted in the literature. However, pervious efforts mainly consider the "check-ins" in a whole and omit their temporal relation. They can only recommend POI globally and cannot know where a user would like to go tomorrow or in the next few days. In this paper, we consider the task of successive personalized POI recommendation in LBSNs, which is a much harder task than standard personalized POI recommendation or prediction. To solve this task, we observe two prominent properties in the check-in sequence: personalized Markov chain and region localization. Hence, we propose a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions. Our proposed FPMC-LR not only exploits the personalized Markov chain in the check-in sequence, but also takes into account users' movement constraint, i.e., moving around a localized region. More importantly, utilizing the information of localized regions, we not only reduce the computation cost largely, but also discard the noisy information to boost recommendation. Results on two real-world LBSNs datasets demonstrate the merits of our proposed FPMC-LR.

522 citations

Cites background or methods or result from "Fused matrix factorization with geo..."

  • ...The other line of work focuses on LBSN data, which is very sparse and large-scale [Ye et al., 2010; 2011; Cheng et al., 2012]....

    [...]

  • ...…e.g., modeling the check-in probability to the distance of the whole check-in history by power-law distribution [Ye et al., 2011], modeling users’ multi-center check-in behaviors via multicenter Gaussians [Cheng et al., 2012], and etc., have been addressed and fused with traditional CF algorithms....

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  • ..., 2011], modeling users’ multi-center check-in behaviors via multicenter Gaussians [Cheng et al., 2012], and etc....

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  • ..., 2011b] and the Gowalla data from [Cheng et al., 2012]....

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  • ...Currently, geographical influence, e.g., modeling the check-in probability to the distance of the whole check-in history by power-law distribution [Ye et al., 2011], modeling users’ multi-center check-in behaviors via multicenter Gaussians [Cheng et al., 2012], and etc., have been addressed and fused with traditional CF algorithms....

    [...]

References
More filters
Proceedings Article•
Probabilistic Matrix Factorization

[...]

Andriy Mnih1, Ruslan Salakhutdinov1•
University of Toronto1
03 Dec 2007
TL;DR: The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.
Abstract: Many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings. In this paper we present the Probabilistic Matrix Factorization (PMF) model which scales linearly with the number of observations and, more importantly, performs well on the large, sparse, and very imbalanced Netflix dataset. We further extend the PMF model to include an adaptive prior on the model parameters and show how the model capacity can be controlled automatically. Finally, we introduce a constrained version of the PMF model that is based on the assumption that users who have rated similar sets of movies are likely to have similar preferences. The resulting model is able to generalize considerably better for users with very few ratings. When the predictions of multiple PMF models are linearly combined with the predictions of Restricted Boltzmann Machines models, we achieve an error rate of 0.8861, that is nearly 7% better than the score of Netflix's own system.

4,022 citations

"Fused matrix factorization with geo..." refers background or methods in this paper

  • ...PMF: this is a well-known method in matrix factorization (Salakhutdinov and Mnih 2007)....

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  • ...Probabilistic Matrix Factorization (PMF) PMF is one of the most famous MF models in collaborative filtering (Salakhutdinov and Mnih 2007)....

    [...]

  • ...Matrix Factorization (MF) is one of the most popular methods for recommender systems (Salakhutdinov and Mnih 2007; 2008; Bell, Koren, and Volinsky 2007; Koren 2009)....

    [...]

  • ...The training time for the matrix factorization models scales linearly with the number of observations (Salakhutdinov and Mnih 2007; Ma et al. 2011b)....

    [...]

Proceedings Article•DOI•
Friendship and mobility: user movement in location-based social networks

[...]

Eunjoon Cho1, Seth A. Myers1, Jure Leskovec1•
Stanford University1
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

"Fused matrix factorization with geo..." refers background or methods or result in this paper

  • ...This indicates that less than 10% of a user’s check-ins are also visited by his/her friends, which is similar to the statistic reported in (Cho, Myers, and Leskovec 2011)....

    [...]

  • ...In addition, our statistic is also a little different from the two states (“home” and “office”) check-in behavior mentioned in (Cho, Myers, and Leskovec 2011)....

    [...]

  • ...The statistics are a little different from those in (Cho, Myers, and Leskovec 2011), but the overall trend is similar....

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  • ...This means that users usually visit several important places, e.g., home, office,and some stores or bars, with very high frequency, while most of other places are seldom visited....

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Proceedings Article•DOI•
Mining interesting locations and travel sequences from GPS trajectories

[...]

Yu Zheng1, Lizhu Zhang1, Xing Xie1, Wei-Ying Ma1•
Microsoft1
20 Apr 2009
TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.
Abstract: The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.

1,903 citations

Proceedings Article•DOI•
Collaborative filtering with temporal dynamics

[...]

Yehuda Koren1•
Yahoo!1
28 Jun 2009
TL;DR: Two leading collaborative filtering recommendation approaches are revamp and a more sensitive approach is required, which can make better distinctions between transient effects and long term patterns.
Abstract: Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.

1,621 citations

"Fused matrix factorization with geo..." refers methods in this paper

  • ...To solve large-scale recommendation problems, matrix factorization is a promising tool due to its success in Netflix competition (Bell, Koren, and Volinsky 2007; Koren 2009)....

    [...]

  • ...To solve large-scale recommendation problems, matrix factorizati on is a promising tool due to its success in Netflix competition (Bell, Koren, and Volinsky 2007; Koren 2009)....

    [...]

  • ...Matrix Factorization (MF) is one of the most popular methods for recommender systems (Salakhutdinov and Mnih 2007; 2008; Bell, Koren, and Volinsky 2007; Koren 2009)....

    [...]

Proceedings Article•DOI•
Recommender systems with social regularization

[...]

Hao Ma1, Dengyong Zhou2, Chao Liu2, Michael R. Lyu1, Irwin King3 •
The Chinese University of Hong Kong1, Microsoft2, AT&T Labs3
09 Feb 2011
TL;DR: This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods.
Abstract: Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.

1,573 citations

"Fused matrix factorization with geo..." refers background or methods in this paper

  • ...We adopts the PMF with Social Regularization (PMFSR) (Ma et al. 2011b), whose objective function is defined as follows: min U,L Ω(U,L) = |U|∑ i=1 |L|∑ j=1 Iij(g(Fij)− g(U T i Lj)) 2 + β |U|∑ i=1 ∑ f∈F(i) Sim(i, f)‖Ui − Uf‖ 2 F + λ1‖U‖ 2 F + λ2‖L‖ 2 F , (4) whereF(i) is the set of friends for…...

    [...]

  • ...We adopts the PMF with Social Regularization (PMFSR) (Ma et al. 2011b), whose objective function is defined as follows:...

    [...]

  • ...PMF with Social Regularization (PMFSR): this method is proposed to include the social friendship under the PMF framework (Ma et al. 2011b)....

    [...]

  • ...• PMFSR attains a little better results than those of PMF....

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  • ...4), we turn to Probabilistic Factor Models (PFM) (Chen et al. 2009; Ma et al. 2011a), which can model the frequency data directly....

    [...]

Related Papers (5)
Exploiting geographical influence for collaborative point-of-interest recommendation

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24 Jul 2011
Mao Ye, Peifeng Yin, Wang-Chien Lee, Dik Lun Lee 
Friendship and mobility: user movement in location-based social networks

[...]

21 Aug 2011
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Time-aware point-of-interest recommendation

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Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, Nadia Magnenat Thalmann 
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation

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Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, Yong Rui 
Where you like to go next: successive point-of-interest recommendation

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03 Aug 2013
Chen Cheng, Haiqin Yang, Michael R. Lyu, Irwin King