6,662 research outputs found
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
Albeit, the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be straightforwardly transformed to the implicit case if
scalability should be maintained. There are few if any implicit feedback
benchmark datasets, therefore new ideas are usually experimented on explicit
benchmarks. In this paper, we propose a generic context-aware implicit feedback
recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor
factorization learning method that scales linearly with the number of non-zero
elements in the tensor. The method also allows us to incorporate diverse
context information into the model while maintaining its computational
efficiency. In particular, we present two such context-aware implementation
variants of iTALS. The first incorporates seasonality and enables to
distinguish user behavior in different time intervals. The other views the user
history as sequential information and has the ability to recognize usage
pattern typical to certain group of items, e.g. to automatically tell apart
product types or categories that are typically purchased repetitively
(collectibles, grocery goods) or once (household appliances). Experiments
performed on three implicit datasets (two proprietary ones and an implicit
variant of the Netflix dataset) show that by integrating context-aware
information with our factorization framework into the state-of-the-art implicit
recommender algorithm the recommendation quality improves significantly.Comment: Accepted for ECML/PKDD 2012, presented on 25th September 2012,
Bristol, U
Context-aware multi-head self-attentional neural network model for next location prediction
Accurate activity location prediction is a crucial component of many mobility
applications and is particularly required to develop personalized, sustainable
transportation systems. Despite the widespread adoption of deep learning
models, next location prediction models lack a comprehensive discussion and
integration of mobility-related spatio-temporal contexts. Here, we utilize a
multi-head self-attentional (MHSA) neural network that learns location
transition patterns from historical location visits, their visit time and
activity duration, as well as their surrounding land use functions, to infer an
individual's next location. Specifically, we adopt point-of-interest data and
latent Dirichlet allocation for representing locations' land use contexts at
multiple spatial scales, generate embedding vectors of the spatio-temporal
features, and learn to predict the next location with an MHSA network. Through
experiments on two large-scale GNSS tracking datasets, we demonstrate that the
proposed model outperforms other state-of-the-art prediction models, and reveal
the contribution of various spatio-temporal contexts to the model's
performance. Moreover, we find that the model trained on population data
achieves higher prediction performance with fewer parameters than
individual-level models due to learning from collective movement patterns. We
also reveal mobility conducted in the recent past and one week before has the
largest influence on the current prediction, showing that learning from a
subset of the historical mobility is sufficient to obtain an accurate location
prediction result. We believe that the proposed model is vital for
context-aware mobility prediction. The gained insights will help to understand
location prediction models and promote their implementation for mobility
applications.Comment: updated Discussion section; accepted by Transportation Research Part
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering
Human mobility demonstrates a high degree of regularity, which facilitates
the discovery of lifestyle profiles. Existing research has yet to fully utilize
the regularities embedded in high-order features extracted from human mobility
records in such profiling. This study proposes a progressive feature extraction
strategy that mines high-order mobility features from users' moving trajectory
records from the spatial, temporal, and semantic dimensions. Specific features
are extracted such as travel motifs, rhythms decomposed by discrete Fourier
transform (DFT) of mobility time series, and vectorized place semantics by
word2vec, respectively to the three dimensions, and they are further clustered
to reveal the users' lifestyle characteristics. An experiment using a
trajectory dataset of over 500k users in Shenzhen, China yields seven user
clusters with different lifestyle profiles that can be well interpreted by
common sense. The results suggest the possibility of fine-grained user
profiling through cross-order trajectory feature engineering and clustering
High school chemistry students\u27 learning of the elements, structure, and periodicity of the periodic table: contributions of inquiry-based activities and exemplary graphics
The main research question of this study was: How do selected high school chemistry students\u27 understandings of the elements, structure, and periodicity of the Periodic Table change as they participate in a unit study consisting of inquiry-based activities emphasizing construction of innovative science graphics? The research question was answered using a multiple case study/mixed model design which employed elements of both qualitative and quantitative methodologies during data collection and analyses. The unit study was conducted over a six-week period with 11th-grade students enrolled in a chemistry class. A purposive sample of six students from the class was selected to participate in interviews and concept map coconstruction (Wandersee & Abrams, 1993) periodically across the study. The progress of the selected students of the case study was compared to the progress of the class as a whole. The students of the case study were also compared to a group of high school chemistry students at a comparative school. The results show that the students from both schools left traditional instruction on the periodic table (lecture and textbook activities) with a very limited understanding of the topic. It also revealed that the inquiry-based, visual approach of the unit study helped students make significant conceptual progress in their understanding of the periodic table. The pictorial periodic table (which features photographs of the elements), used in conjunction with the graphic technique of data mapping, enhanced students understanding of the patterns of the physical properties of the elements on the periodic table. The graphic technique of compound mapping helped students learn reactivity patterns between types and groups of elements on the periodic table. The recreation of the periodic table with element cards created from the pictorial periodic table helped students progress in their understanding of periodicity and its key concepts. The Periodic Table Literacy Rubric (PTLR) proved to be a valuable tool for assessing students’ conceptual progress, and helped to identify a critical juncture in the learning of periodicity. In addition, the PTLR rubric\u27s historical-conceptual design demonstrates how the history of science can be used to inform today\u27s science teaching
Time-aware metric embedding with asymmetric projection for successive POI recommendation
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall
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