4,178 research outputs found
GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation
Point-of-interest (POI) recommendation is an important application in
location-based social networks (LBSNs), which learns the user preference and
mobility pattern from check-in sequences to recommend POIs. However, previous
POI recommendation systems model check-in sequences based on either tensor
factorization or Markov chain model, which cannot capture contextual check-in
information in sequences. The contextual check-in information implies the
complementary functions among POIs that compose an individual's daily check-in
sequence. In this paper, we exploit the embedding learning technique to capture
the contextual check-in information and further propose the
\textit{{\textbf{SE}}}quential \textit{{\textbf{E}}}mbedding
\textit{{\textbf{R}}}ank (\textit{SEER}) model for POI recommendation. In
particular, the \textit{SEER} model learns user preferences via a pairwise
ranking model under the sequential constraint modeled by the POI embedding
learning method. Furthermore, we incorporate two important factors, i.e.,
temporal influence and geographical influence, into the \textit{SEER} model to
enhance the POI recommendation system. Due to the temporal variance of
sequences on different days, we propose a temporal POI embedding model and
incorporate the temporal POI representations into a temporal preference ranking
model to establish the \textit{T}emporal \textit{SEER} (\textit{T-SEER}) model.
In addition, We incorporate the geographical influence into the \textit{T-SEER}
model and develop the \textit{\textbf{Geo-Temporal}} \textit{{\textbf{SEER}}}
(\textit{GT-SEER}) model
A Survey of Point-of-interest Recommendation in Location-based Social Networks
Point-of-interest (POI) recommendation that suggests new places for users to
visit arises with the popularity of location-based social networks (LBSNs). Due
to the importance of POI recommendation in LBSNs, it has attracted much
academic and industrial interest. In this paper, we offer a systematic review
of this field, summarizing the contributions of individual efforts and
exploring their relations. We discuss the new properties and challenges in POI
recommendation, compared with traditional recommendation problems, e.g., movie
recommendation. Then, we present a comprehensive review in three aspects:
influential factors for POI recommendation, methodologies employed for POI
recommendation, and different tasks in POI recommendation. Specifically, we
propose three taxonomies to classify POI recommendation systems. First, we
categorize the systems by the influential factors check-in characteristics,
including the geographical information, social relationship, temporal
influence, and content indications. Second, we categorize the systems by the
methodology, including systems modeled by fused methods and joint methods.
Third, we categorize the systems as general POI recommendation and successive
POI recommendation by subtle differences in the recommendation task whether to
be bias to the recent check-in. For each category, we summarize the
contributions and system features, and highlight the representative work.
Moreover, we discuss the available data sets and the popular metrics. Finally,
we point out the possible future directions in this area and conclude this
survey
Personalized Context-Aware Point of Interest Recommendation
Personalized recommendation of Points of Interest (POIs) plays a key role in
satisfying users on Location-Based Social Networks (LBSNs). In this paper, we
propose a probabilistic model to find the mapping between user-annotated tags
and locations' taste keywords. Furthermore, we introduce a dataset on
locations' contextual appropriateness and demonstrate its usefulness in
predicting the contextual relevance of locations. We investigate four
approaches to use our proposed mapping for addressing the data sparsity
problem: one model to reduce the dimensionality of location taste keywords and
three models to predict user tags for a new location. Moreover, we present
different scores calculated from multiple LBSNs and show how we incorporate new
information from the mapping into a POI recommendation approach. Then, the
computed scores are integrated using learning to rank techniques. The
experiments on two TREC datasets show the effectiveness of our approach,
beating state-of-the-art methods.Comment: To appear at ACM Transactions on Information Systems (TOIS
Utilizing FastText for Venue Recommendation
Venue recommendation systems model the past interactions (i.e., check-ins) of
the users and recommend venues. Traditional recommendation systems employ
collaborative filtering, content-based filtering or matrix factorization.
Recently, vector space embedding and deep learning algorithms are also used for
recommendation. In this work, I propose a method for recommending top-k venues
by utilizing the sequentiality feature of check-ins and a recent vector space
embedding method, namely the FastText. Our proposed method; forms groups of
check-ins, learns the vector space representations of the venues and utilizes
the learned embeddings to make venue recommendations. I measure the performance
of the proposed method using a Foursquare check-in dataset.The results show
that the proposed method performs better than the state-of-the-art methods
A Neural Network Approach to Joint Modeling Social Networks and Mobile Trajectories
The accelerated growth of mobile trajectories in location-based services
brings valuable data resources to understand users' moving behaviors. Apart
from recording the trajectory data, another major characteristic of these
location-based services is that they also allow the users to connect whomever
they like. A combination of social networking and location-based services is
called as location-based social networks (LBSN). As shown in previous works,
locations that are frequently visited by socially-related persons tend to be
correlated, which indicates the close association between social connections
and trajectory behaviors of users in LBSNs. In order to better analyze and mine
LBSN data, we present a novel neural network model which can joint model both
social networks and mobile trajectories. In specific, our model consists of two
components: the construction of social networks and the generation of mobile
trajectories. We first adopt a network embedding method for the construction of
social networks: a networking representation can be derived for a user. The key
of our model lies in the component of generating mobile trajectories. We have
considered four factors that influence the generation process of mobile
trajectories, namely user visit preference, influence of friends, short-term
sequential contexts and long-term sequential contexts. To characterize the last
two contexts, we employ the RNN and GRU models to capture the sequential
relatedness in mobile trajectories at different levels, i.e., short term or
long term. Finally, the two components are tied by sharing the user network
representations. Experimental results on two important applications demonstrate
the effectiveness of our model. Especially, the improvement over baselines is
more significant when either network structure or trajectory data is sparse.Comment: Accepted by ACM TOI
A novel approach for venue recommendation using cross-domain techniques
Finding the next venue to be visited by a user in a specific city is an
interesting, but challenging, problem. Different techniques have been proposed,
combining collaborative, content, social, and geographical signals; however it
is not trivial to decide which tech- nique works best, since this may depend on
the data density or the amount of activity logged for each user or item. At the
same time, cross-domain strategies have been exploited in the recommender
systems literature when dealing with (very) sparse situations, such as those
inherently arising when recommendations are produced based on information from
a single city.
In this paper, we address the problem of venue recommendation from a novel
perspective: applying cross-domain recommenda- tion techniques considering each
city as a different domain. We perform an experimental comparison of several
recommendation techniques in a temporal split under two conditions:
single-domain (only information from the target city is considered) and cross-
domain (information from many other cities is incorporated into the
recommendation algorithm). For the latter, we have explored two strategies to
transfer knowledge from one domain to another: testing the target city and
training a model with information of the k cities with more ratings or only
using the k closest cities.
Our results show that, in general, applying cross-domain by proximity
increases the performance of the majority of the recom- menders in terms of
relevance. This is the first work, to the best of our knowledge, where so many
domains (eight) are combined in the tourism context where a temporal split is
used, and thus we expect these results could provide readers with an overall
picture of what can be achieved in a real-world environment.Comment: Accepted at the Workshop on Intelligent Recommender Systems by
Knowledge Transfer and Learning co-located with the 12th ACM Conference on
Recommender Systems (RecSys 2018
Point-of-Interest Recommender Systems: A Survey from an Experimental Perspective
Point-of-Interest recommendation is an increasing research and developing
area within the widely adopted technologies known as Recommender Systems. Among
them, those that exploit information coming from Location-Based Social Networks
(LBSNs) are very popular nowadays and could work with different information
sources, which pose several challenges and research questions to the community
as a whole. We present a systematic review focused on the research done in the
last 10 years about this topic. We discuss and categorize the algorithms and
evaluation methodologies used in these works and point out the opportunities
and challenges that remain open in the field. More specifically, we report the
leading recommendation techniques and information sources that have been
exploited more often (such as the geographical signal and deep learning
approaches) while we also alert about the lack of reproducibility in the field
that may hinder real performance improvements.Comment: Submitted in Jul 2020 (revised in Jun 2021, still under review) to
ACM Computing Survey
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
In the field of sequential recommendation, deep learning (DL)-based methods
have received a lot of attention in the past few years and surpassed
traditional models such as Markov chain-based and factorization-based ones.
However, there is little systematic study on DL-based methods, especially
regarding to how to design an effective DL model for sequential recommendation.
In this view, this survey focuses on DL-based sequential recommender systems by
taking the aforementioned issues into consideration. Specifically,we illustrate
the concept of sequential recommendation, propose a categorization of existing
algorithms in terms of three types of behavioral sequence, summarize the key
factors affecting the performance of DL-based models, and conduct corresponding
evaluations to demonstrate the effects of these factors. We conclude this
survey by systematically outlining future directions and challenges in this
field.Comment: 36 pages, 17 figures, 6 tables, 104 reference
A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes
Attributes, such as metadata and profile, carry useful information which in
principle can help improve accuracy in recommender systems. However, existing
approaches have difficulty in fully leveraging attribute information due to
practical challenges such as heterogeneity and sparseness. These approaches
also fail to combine recurrent neural networks which have recently shown
effectiveness in item recommendations in applications such as video and music
browsing. To overcome the challenges and to harvest the advantages of sequence
models, we present a novel approach, Heterogeneous Attribute Recurrent Neural
Networks (HA-RNN), which incorporates heterogeneous attributes and captures
sequential dependencies in \textit{both} items and attributes. HA-RNN extends
recurrent neural networks with 1) a hierarchical attribute combination input
layer and 2) an output attribute embedding layer. We conduct extensive
experiments on two large-scale datasets. The new approach show significant
improvements over the state-of-the-art models. Our ablation experiments
demonstrate the effectiveness of the two components to address heterogeneous
attribute challenges including variable lengths and attribute sparseness. We
further investigate why sequence modeling works well by conducting exploratory
studies and show sequence models are more effective when data scale increases.Comment: A shorter version appeared in ICDM 2017 SERecsys worksho
Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling
Many social network applications depend on robust representations of
spatio-temporal data. In this work, we present an embedding model based on
feed-forward neural networks which transforms social media check-ins into dense
feature vectors encoding geographic, temporal, and functional aspects for
modelling places, neighborhoods, and users. We employ the embedding model in a
variety of applications including location recommendation, urban functional
zone study, and crime prediction. For location recommendation, we propose a
Spatio-Temporal Embedding Similarity algorithm (STES) based on the embedding
model.
In a range of experiments on real life data collected from Foursquare, we
demonstrate our model's effectiveness at characterizing places and people and
its applicability in aforementioned problem domains. Finally, we select eight
major cities around the globe and verify the robustness and generality of our
model by porting pre-trained models from one city to another, thereby
alleviating the need for costly local training
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