7 research outputs found
Category-Aware Location Embedding for Point-of-Interest Recommendation
Recently, Point of interest (POI) recommendation has gained ever-increasing
importance in various Location-Based Social Networks (LBSNs). With the recent
advances of neural models, much work has sought to leverage neural networks to
learn neural embeddings in a pre-training phase that achieve an improved
representation of POIs and consequently a better recommendation. However,
previous studies fail to capture crucial information about POIs such as
categorical information.
In this paper, we propose a novel neural model that generates a POI embedding
incorporating sequential and categorical information from POIs. Our model
consists of a check-in module and a category module. The check-in module
captures the geographical influence of POIs derived from the sequence of users'
check-ins, while the category module captures the characteristics of POIs
derived from the category information. To validate the efficacy of the model,
we experimented with two large-scale LBSN datasets. Our experimental results
demonstrate that our approach significantly outperforms state-of-the-art POI
recommendation methods.Comment: 4 pages, 1 figure
Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs
An increasing amount of location-based service (LBS) data is being
accumulated and helps to study urban dynamics and human mobility. GPS
coordinates and other location indicators are normally low dimensional and only
representing spatial proximity, thus difficult to be effectively utilized by
machine learning models in Geo-aware applications. Existing location embedding
methods are mostly tailored for specific problems that are taken place within
areas of interest. When it comes to the scale of a city or even a country,
existing approaches always suffer from extensive computational cost and
significant data sparsity. Different from existing studies, we propose to learn
representations through a GCN-aided skip-gram model named GCN-L2V by
considering both spatial connection and human mobility. With a flow graph and a
spatial graph, it embeds context information into vector representations.
GCN-L2V is able to capture relationships among locations and provide a better
notion of similarity in a spatial environment. Across quantitative experiments
and case studies, we empirically demonstrate that representations learned by
GCN-L2V are effective. As far as we know, this is the first study that provides
a fine-grained location embedding at the city level using only LBS records.
GCN-L2V is a general-purpose embedding model with high flexibility and can be
applied in down-streaming Geo-aware applications
City2City: Translating Place Representations across Cities
Large mobility datasets collected from various sources have allowed us to
observe, analyze, predict and solve a wide range of important urban challenges.
In particular, studies have generated place representations (or embeddings)
from mobility patterns in a similar manner to word embeddings to better
understand the functionality of different places within a city. However,
studies have been limited to generating such representations of cities in an
individual manner and has lacked an inter-city perspective, which has made it
difficult to transfer the insights gained from the place representations across
different cities. In this study, we attempt to bridge this research gap by
treating \textit{cities} and \textit{languages} analogously. We apply methods
developed for unsupervised machine language translation tasks to translate
place representations across different cities. Real world mobility data
collected from mobile phone users in 2 cities in Japan are used to test our
place representation translation methods. Translated place representations are
validated using landuse data, and results show that our methods were able to
accurately translate place representations from one city to another.Comment: A short 4-page version of this work was accepted in ACM SIGSPATIAL
Conference 2019. This is the full version with details. In Proceedings of the
27th ACM SIGSPATIAL International Conference on Advances in Geographic
Information Systems. AC
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
Existing spatial object recommendation algorithms generally treat objects
identically when ranking them. However, spatial objects often cover different
levels of spatial granularity and thereby are heterogeneous. For example, one
user may prefer to be recommended a region (say Manhattan), while another user
might prefer a venue (say a restaurant). Even for the same user, preferences
can change at different stages of data exploration. In this paper, we study how
to support top-k spatial object recommendations at varying levels of spatial
granularity, enabling spatial objects at varying granularity, such as a city,
suburb, or building, as a Point of Interest (POI). To solve this problem, we
propose the use of a POI tree, which captures spatial containment relationships
between POIs. We design a novel multi-task learning model called MPR (short for
Multi-level POI Recommendation), where each task aims to return the top-k POIs
at a certain spatial granularity level. Each task consists of two subtasks: (i)
attribute-based representation learning; (ii) interaction-based representation
learning. The first subtask learns the feature representations for both users
and POIs, capturing attributes directly from their profiles. The second subtask
incorporates user-POI interactions into the model. Additionally, MPR can
provide insights into why certain recommendations are being made to a user
based on three types of hints: user-aspect, POI-aspect, and interaction-aspect.
We empirically validate our approach using two real-life datasets, and show
promising performance improvements over several state-of-the-art methods