542 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Timestamps as Prompts for Geography-Aware Location Recommendation
Location recommendation plays a vital role in improving users' travel
experience. The timestamp of the POI to be predicted is of great significance,
since a user will go to different places at different times. However, most
existing methods either do not use this kind of temporal information, or just
implicitly fuse it with other contextual information. In this paper, we revisit
the problem of location recommendation and point out that explicitly modeling
temporal information is a great help when the model needs to predict not only
the next location but also further locations. In addition, state-of-the-art
methods do not make effective use of geographic information and suffer from the
hard boundary problem when encoding geographic information by gridding. To this
end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed.
The temporal prompt is firstly designed to incorporate temporal information of
any further check-in. A shifted window mechanism is then devised to augment
geographic data for addressing the hard boundary problem. Via extensive
comparisons with existing methods and ablation studies on five real-world
datasets, we demonstrate the effectiveness and superiority of the proposed
method under various settings. Most importantly, our proposed model has the
superior ability of interval prediction. In particular, the model can predict
the location that a user wants to go to at a certain time while the most recent
check-in behavioral data is masked, or it can predict specific future check-in
(not just the next one) at a given timestamp
Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network
Human mobility prediction is a fundamental task essential for various
applications, including urban planning, location-based services and intelligent
transportation systems. Existing methods often ignore activity information
crucial for reasoning human preferences and routines, or adopt a simplified
representation of the dependencies between time, activities and locations. To
address these issues, we present Hierarchical Graph Attention Recurrent Network
(HGARN) for human mobility prediction. Specifically, we construct a
hierarchical graph based on all users' history mobility records and employ a
Hierarchical Graph Attention Module to capture complex time-activity-location
dependencies. This way, HGARN can learn representations with rich human travel
semantics to model user preferences at the global level. We also propose a
model-agnostic history-enhanced confidence (MAHEC) label to focus our model on
each user's individual-level preferences. Finally, we introduce a Temporal
Module, which employs recurrent structures to jointly predict users' next
activities (as an auxiliary task) and their associated locations. By leveraging
the predicted future user activity features through a hierarchical and residual
design, the accuracy of the location predictions can be further enhanced. For
model evaluation, we test the performances of our HGARN against existing SOTAs
in both the recurring and explorative settings. The recurring setting focuses
on assessing models' capabilities to capture users' individual-level
preferences, while the results in the explorative setting tend to reflect the
power of different models to learn users' global-level preferences. Overall,
our model outperforms other baselines significantly in all settings based on
two real-world human mobility data benchmarks. Source codes of HGARN are
available at https://github.com/YihongT/HGARN.Comment: 11 page
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
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
A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
Tourism is an important application domain for recommender systems. In this
domain, recommender systems are for example tasked with providing personalized
recommendations for transportation, accommodation, points-of-interest (POIs),
or tourism services. Among these tasks, in particular the problem of
recommending POIs that are of likely interest to individual tourists has gained
growing attention in recent years. Providing POI recommendations to tourists
\emph{during their trip} can however be especially challenging due to the
variability of the users' context. With the rapid development of the Web and
today's multitude of online services, vast amounts of data from various sources
have become available, and these heterogeneous data sources represent a huge
potential to better address the challenges of in-trip POI recommendation
problems. In this work, we provide a comprehensive survey of published research
on POI recommendation between 2017 and 2022 from the perspective of
heterogeneous data sources. Specifically, we investigate which types of data
are used in the literature and which technical approaches and evaluation
methods are predominant. Among other aspects, we find that today's research
works often focus on a narrow range of data sources, leaving great potential
for future works that better utilize heterogeneous data sources and diverse
data types for improved in-trip recommendations.Comment: 35 pages, 19 figure
- …