1,626 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
Relation Structure-Aware Heterogeneous Information Network Embedding
Heterogeneous information network (HIN) embedding aims to embed multiple
types of nodes into a low-dimensional space. Although most existing HIN
embedding methods consider heterogeneous relations in HINs, they usually employ
one single model for all relations without distinction, which inevitably
restricts the capability of network embedding. In this paper, we take the
structural characteristics of heterogeneous relations into consideration and
propose a novel Relation structure-aware Heterogeneous Information Network
Embedding model (RHINE). By exploring the real-world networks with thorough
mathematical analysis, we present two structure-related measures which can
consistently distinguish heterogeneous relations into two categories:
Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the
distinctive characteristics of relations, in our RHINE, we propose different
models specifically tailored to handle ARs and IRs, which can better capture
the structures and semantics of the networks. At last, we combine and optimize
these models in a unified and elegant manner. Extensive experiments on three
real-world datasets demonstrate that our model significantly outperforms the
state-of-the-art methods in various tasks, including node clustering, link
prediction, and node classification
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