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44
Recommending Friends and Locations Based on Individual Location History
, 2008
"... The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply to some extent users ‟ interests in places, and bring us opportunities to understand the correla ..."
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Cited by 67 (14 self)
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The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply to some extent users ‟ interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction, and report on a personalized friend & location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system a particular individual‟s visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories, and recommend each user a group of potential friends in a GIS community. Third, we estimate an individual‟s interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchicalgraph-based similarity measurement (HGSM), is proposed to uniformly model each individual‟s location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people‟s outdoor movements, 2) the visited popularity of a geospatial region and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate
All-at-once Optimization for Coupled Matrix and Tensor Factorizations
, 1105
"... Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers ..."
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Cited by 28 (3 self)
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Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers ’ social networks (e.g., Facebook friendships) and restaurant categories information (e.g., Thai or Italian) can also be used to make better recommendations. The task of fusing data, however, is challenging since data sets can be incomplete and heterogeneous, i.e., data consist of both matrices, e.g., the person by person social network matrix or the restaurant by category matrix, and higher-order tensors, e.g., the “ratings ” tensor of the form restaurant by meal by person. In this paper, we are particularly interested in fusing data sets with the goal of capturing their underlying latent structures. We formulate this problem as a coupled matrix and tensor factorization (CMTF) problem where heterogeneous data sets are modeled by fitting outer-product models to higher-order tensors and matrices in a coupled manner. Unlike traditional approaches solving this problem using alternating algorithms, we propose an all-at-once optimization approach called CMTF-OPT (CMTF-OPTimization), which is a gradient-based optimization approach for joint analysis of matrices and higher-order tensors. We also extend the algorithm to handle coupled incomplete data sets. Using numerical experiments, we demonstrate that the proposed all-at-once approach is more accurate than the alternating least squares approach.
Finding similar users using category-based location history
- In GIS
, 2010
"... In this paper, we aim to estimate the similarity between users according to their GPS trajectories. Our approach first models a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls � restaurants � cinemas. Then, we measure the similarity between different users ’ SLHs ..."
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Cited by 21 (5 self)
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In this paper, we aim to estimate the similarity between users according to their GPS trajectories. Our approach first models a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls � restaurants � cinemas. Then, we measure the similarity between different users ’ SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user’s interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. As a result, SLH-MTM outperforms the related works [4].
Urban Computing: Concepts, Methodologies, and Applications
"... Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geo ..."
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Cited by 14 (7 self)
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Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geographical data. Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people’s lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology, in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Secondly, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety & security, presenting representative scenarios in each category. Thirdly, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we outlook the
iGSLR: Personalized geo-social location recommendation: A kernel density estimation approach
- In ACM SIGSPATIAL
, 2013
"... 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 prefe ..."
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Cited by 13 (8 self)
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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 in
uence, geographical in-uence has also been intensively exploited in the process of location recommendation based on the fact that geographi-cal proximity significantly affects users ’ check-in behaviors. Although geographical influence on users should be person-alized, 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 pref-erence, social in
uence, and personalized geographical in
u-ence are integrated into a unified geo-social recommenda-tion framework. We conduct a comprehensive performance evaluation for iGSLR using two large-scale real data sets collected from Foursquare and Gowalla which are the two of the most popular LBSNs. Experimental results show that iGSLR provides significantly superior location recommen-dation compared to other state-of-the-art geo-social recom-mendation techniques.
Mining personal context-aware preferences for mobile users
- In Proceedings of the IEEE 12th International Conference on Data Mining, ICDM'12
, 2012
"... Abstract—In this paper, we illustrate how to extract personal context-aware preferences from the context-rich device logs (i.e., context logs) for building novel personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may n ..."
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Cited by 9 (6 self)
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Abstract—In this paper, we illustrate how to extract personal context-aware preferences from the context-rich device logs (i.e., context logs) for building novel personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his/her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, con-text independent and context dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world data set show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.
Diagnosing new york city’s noises with ubiquitous data
- In ACM Ubicomp
, 2014
"... ABSTRACT Many cities suffer from noise pollution, which compromises people's working efficiency and even mental health. New York City (NYC) has opened a platform, entitled 311, to allow people to complain about the city's issues by using a mobile app or making a phone call; noise is the t ..."
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Cited by 9 (2 self)
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ABSTRACT Many cities suffer from noise pollution, which compromises people's working efficiency and even mental health. New York City (NYC) has opened a platform, entitled 311, to allow people to complain about the city's issues by using a mobile app or making a phone call; noise is the third largest category of complaints in the 311 data. As each complaint about noises is associated with a location, a time stamp, and a fine-grained noise category, such as "Loud Music" or "Construction", the data is actually a result of "human as a sensor" and "crowd sensing", containing rich human intelligence that can help diagnose urban noises. In this paper we infer the fine-grained noise situation (consisting of a noise pollution indicator and the composition of noises) of different times of day for each region of NYC, by using the 311 complaint data together with social media, road network data, and Points of Interests (POIs). We model the noise situation of NYC with a three dimension tensor, where the three dimensions stand for regions, noise categories, and time slots, respectively. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we recover the noise situation throughout NYC. The information can inform people and officials' decision making. We evaluate our method with four real datasets, verifying the advantages of our method beyond four baselines, such as the interpolation-based approach.
Tutorial on Location-Based Social Networks
- In proceeding of International conference on World Wide Web
"... This paper is an abstract of a tutorial on location-based social networks (LBSNs), introducing the concept, unique features, and research philosophy of LBSNs. The slide deck of this tutorial can be found on ..."
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Cited by 7 (1 self)
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This paper is an abstract of a tutorial on location-based social networks (LBSNs), introducing the concept, unique features, and research philosophy of LBSNs. The slide deck of this tutorial can be found on
A habit mining approach for discovering similar mobile users
- in Proc. WWW
, 2012
"... Discovering similar users with respect to their habits plays an important role in a wide range of applications, such as collaborative filtering for recommendation, user segmenta-tion for market analysis, etc. Recently, the progressing abil-ity to sense user contexts of smart mobile devices makes it ..."
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Cited by 6 (2 self)
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Discovering similar users with respect to their habits plays an important role in a wide range of applications, such as collaborative filtering for recommendation, user segmenta-tion for market analysis, etc. Recently, the progressing abil-ity to sense user contexts of smart mobile devices makes it possible to discover mobile users with similar habits by min-ing their habits from their mobile devices. However, though some researchers have proposed effective methods for mining user habits such as behavior pattern mining, how to lever-age the mined results for discovering similar users remains less explored. To this end, we propose a novel approach for conquering the sparseness of behavior pattern space and thus make it possible to discover similar mobile users with respect to their habits by leveraging behavior pattern min-ing. To be specific, first, we normalize the raw context log of each user by transforming the location-based context data and user interaction records to more general representation-s. Second, we take advantage of a constraint-based Bayesian Matrix Factorization model for extracting the latent com-mon habits among behavior patterns and then transforming behavior pattern vectors to the vectors of mined common habits which are in a much more dense space. The experi-ments conducted on real data sets show that our approach outperforms three baselines in terms of the effectiveness of discovering similar mobile users with respect to their habit-s.
prediction via generalized coupled tensor factorisation
- in ECML/PKDDWorkshop on Collective Learning and Inference on Structured Data
, 2012
"... Abstract. This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest. We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of ..."
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Cited by 4 (1 self)
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Abstract. This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest. We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of matrices and higher-order tensors. We propose to use an approach based on probabilistic interpretation of tensor factorisation models, i.e., Generalised Coupled Tensor Factorisation, which can simultaneously fit a large class of tensor models to higher-order tensors/matrices with com-mon latent factors using different loss functions. Numerical experiments demonstrate that joint analysis of data from multiple sources via coupled factorisation improves the link prediction performance and the selection of right loss function and tensor model is crucial for accurately predicting missing links.
