Abstract: With the rapid development of location-based social networks
(LBSNs), spatial item recommendation has become an important way of helping
users discover interesting locations to increase their engagement with
location-based services. Although human movement exhibits sequential patterns
in LBSNs, most current studies on spatial item recommendations do not consider
the sequential influence of locations. Leveraging sequential patterns in
spatial item recommendation is, however, very challenging, considering 1)
users’ check-in data in LBSNs has a low sampling rate in both space and time,
which renders existing prediction techniques on GPS trajectories ineffective;
2) the prediction space is extremely large, with millions of distinct locations
as the next prediction target, which impedes the application of classical
Markov chain models; and 3) there is no existing framework that unifies users’
personal interests and the sequential influence in a principled manner.In light
of the above challenges, we propose a sequential personalized spatial item
recommendation framework (SPORE) which introduces a novel latent variable
topic-region to model and fuse sequential influence with personal interests in
the latent and exponential space. The advantages of modeling the sequential
effect at the topic-region level include a significantly reduced prediction
space, an effective alleviation of data sparsity and a direct expression of the
semantic meaning of users’ spatial activities. Furthermore, we design an
asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online
top-k recommendation process by extending the traditional LSH. We evaluate the
performance of SPORE on two real datasets and one large-scale synthetic
dataset. The results demonstrate a significant improvement in SPORE’s ability
to recommend spatial items, in terms of both effectiveness and efficiency,
compared with the state-of-the-art methods.
IEEE 2016 : Truth Discovery in Crowdsourced Detection of Spatial Events
Abstract:The ubiquity of smartphones has led to the emergence of mobile
crowdsourcing tasks such as the detection of spatial events when smartphone
users move around in their daily lives. However, the credibility of those
detected events can be negatively impacted by unreliable participants with
low-quality data. Consequently, a major challenge in quality control is to
discover true events from diverse and noisy participants’ reports. This truth
discovery problem is uniquely distinct from its online counterpart in that it
involves uncertainties in both participants’ mobility and reliability.
Decoupling these two types of uncertainties through location tracking will
raise severe privacy and energy issues, whereas simply ignoring missing reports
or treating them as negative reports will significantly degrade the accuracy of
the discovered truth. In this paper, we propose a new method to tackle this
truth discovery problem through principled probabilistic modeling. In
particular, we integrate the modeling of location popularity, location visit
indicators, truth of events and three-way participant reliability in a unified
framework. The proposed model is thus capable of efficiently handling various
types of uncertainties and automatically discovering truth without any supervision
or the need of location tracking. Experimental results demonstrate that our
proposed method outperforms existing state-of-the-art truth discovery
approaches in the mobile crowdsourcing environment.
IEEE 2016 : Sentiment Analysis of Top Colleges in India Using Twitter
Data
Abstract: Ttoday’s
world, opinions and reviews accessible to us are one of the most critical
factors in formulating our views and influencing the success of a brand,
product or service. With the advent and growth of social media in the world,
stakeholders often take to expressing their opinions on popular social media,
namely Twitter. While Twitter data is extremely informative, it presents a
challenge for analysis because of its humongous and disorganized nature. This
paper is a thorough effort to dive into the novel domain of performing
sentiment analysis of people’s opinions regarding top colleges in India.
Besides taking additional preprocessing measures like the expansion of net
lingo and removal of duplicate tweets, a probabilistic model based on Bayes’
theorem was used for spelling correction, which is overlooked in other research
studies. This paper also highlights a comparison between the results obtained
by exploiting the following machine learning algorithms: Naïve Bayes and
Support Vector Machine and an Artificial Neural Network model: Multilayer
Perceptron. Furthermore, a contrast has been presented between four different
kernels of SVM: RBF, linear, polynomial and sigmoid.
IEEE 2016 : FRAppE: Detecting Malicious Facebook Applications
Abstract:With 20 million installs a day [1], third-party apps are a major
reason for the popularity and addictiveness of Facebook. Unfortunately, hackers
have realized the potential of using apps for spreading malware and spam. The
problem is already significant, as we find that at least 13% of apps in our
dataset are malicious. So far,the research community has focused on detecting
malicious posts and campaigns. In this paper, we ask the question: given a
Facebook application, can we determine if it is malicious? Our key contribution
is in developing FRAppE—Facebook’s Rigorous Application Evaluator— arguably the
first tool focused on detecting malicious apps on Facebook. To develop FRAppE,
we use information gathered by observing the posting behavior of 111K Facebook
apps seen across 2.2 million users on Facebook. First, we identify a set of
features that help us distinguish malicious apps from benign ones. For
example, we find that malicious apps often share names with other apps, and
they typically request fewer permissions than benign apps. Second, leveraging
these distinguishing features, we show that FRAppE can detect malicious apps
with 99.5% accuracy, with no false positives and a low false negative rate
(4.1%). Finally, we explore the ecosystem of malicious Facebook apps and
identify mechanisms that these apps use to propagate. Interestingly, we find
that many apps collude and support each other; in our dataset, we find 1,584
apps enabling the viral propagation of 3,723 other apps through their posts.
Long-term, we see FRAppE as a step towards creating an independent watchdog for
app assessment and ranking,so as to warn Facebook users before installing apps.
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