12,503 research outputs found
GhostLink: Latent Network Inference for Influence-aware Recommendation
Social influence plays a vital role in shaping a user's behavior in online
communities dealing with items of fine taste like movies, food, and beer. For
online recommendation, this implies that users' preferences and ratings are
influenced due to other individuals. Given only time-stamped reviews of users,
can we find out who-influences-whom, and characteristics of the underlying
influence network? Can we use this network to improve recommendation?
While prior works in social-aware recommendation have leveraged social
interaction by considering the observed social network of users, many
communities like Amazon, Beeradvocate, and Ratebeer do not have explicit
user-user links. Therefore, we propose GhostLink, an unsupervised probabilistic
graphical model, to automatically learn the latent influence network underlying
a review community -- given only the temporal traces (timestamps) of users'
posts and their content. Based on extensive experiments with four real-world
datasets with 13 million reviews, we show that GhostLink improves item
recommendation by around 23% over state-of-the-art methods that do not consider
this influence. As additional use-cases, we show that GhostLink can be used to
differentiate between users' latent preferences and influenced ones, as well as
to detect influential users based on the learned influence graph
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 Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Unveiling Contextual Similarity of Things via Mining Human-Thing Interactions in the Internet of Things
With recent advances in radio-frequency identification (RFID), wireless
sensor networks, and Web services, physical things are becoming an integral
part of the emerging ubiquitous Web. Finding correlations of ubiquitous things
is a crucial prerequisite for many important applications such as things
search, discovery, classification, recommendation, and composition. This
article presents DisCor-T, a novel graph-based method for discovering
underlying connections of things via mining the rich content embodied in
human-thing interactions in terms of user, temporal and spatial information. We
model these various information using two graphs, namely spatio-temporal graph
and social graph. Then, random walk with restart (RWR) is applied to find
proximities among things, and a relational graph of things (RGT) indicating
implicit correlations of things is learned. The correlation analysis lays a
solid foundation contributing to improved effectiveness in things management.
To demonstrate the utility, we develop a flexible feature-based classification
framework on top of RGT and perform a systematic case study. Our evaluation
exhibits the strength and feasibility of the proposed approach
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
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
Mobile Information Retrieval
Mobile Information Retrieval (Mobile IR) is a relatively recent branch of
Information Retrieval (IR) that is concerned with enabling users to carry out,
using a mobile device, all the classical IR operations that they were used to
carry out on a desktop. This includes finding content available on local
repositories or on the web in response to a user query, interacting with the
system in an explicit or implicit way, reformulate the query and/or visualise
the content of the retrieved documents, as well as providing relevance
judgments to improve the retrieval process.
This book is structured as follows. Chapter 2 provides a very brief overview
of IR and of Mobile IR, briefly outlining what in Mobile IR is different from
IR. Chapter 3 provides the foundations of Mobile IR, looking at the
characteristics of mobile devices and what they bring to IR, but also looking
at how the concept of relevance changed from standard IR to Mobile IR. Chapter
4 presents an overview of the document collections that are searchable by a
Mobile IR system, and that are somehow different from classical IR ones;
available for experimentation, including collections of data that have become
complementary to Mobile IR. Similarly, Chapter 5 reviews mobile information
needs studies and users log analysis. Chapter 6 reviews studies aimed at
adapting and improving the users interface to the needs of Mobile IR. Chapter
7, instead, reviews work on context awareness, which studies the many aspects
of the user context that Mobile IR employs. Chapter 8 reviews some of
evaluation work done in Mobile IR, highlighting the distinctions with classical
IR from the perspectives of two main IR evaluation methodologies: users studies
and test collections. Finally, Chapter 9 reports the conclusions of this
review, highlighting briefly some trends in Mobile IR that we believe will
drive research in the next few years.Comment: 116 pages, published in 201
A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
Recommender systems (RSs) provide an effective way of alleviating the
information overload problem by selecting personalized choices. Online social
networks and user-generated content provide diverse sources for recommendation
beyond ratings, which present opportunities as well as challenges for
traditional RSs. Although social matrix factorization (Social MF) can integrate
ratings with social relations and topic matrix factorization can integrate
ratings with item reviews, both of them ignore some useful information. In this
paper, we investigate the effective data fusion by combining the two
approaches, in two steps. First, we extend Social MF to exploit the graph
structure of neighbors. Second, we propose a novel framework MR3 to jointly
model these three types of information effectively for rating prediction by
aligning latent factors and hidden topics. We achieve more accurate rating
prediction on two real-life datasets. Furthermore, we measure the contribution
of each data source to the proposed framework.Comment: 7 pages, 8 figure
Link Prediction in Social Networks: the State-of-the-Art
In social networks, link prediction predicts missing links in current
networks and new or dissolution links in future networks, is important for
mining and analyzing the evolution of social networks. In the past decade, many
works have been done about the link prediction in social networks. The goal of
this paper is to comprehensively review, analyze and discuss the
state-of-the-art of the link prediction in social networks. A systematical
category for link prediction techniques and problems is presented. Then link
prediction techniques and problems are analyzed and discussed. Typical
applications of link prediction are also addressed. Achievements and roadmaps
of some active research groups are introduced. Finally, some future challenges
of the link prediction in social networks are discussed.Comment: 38 pages, 13 figures, Science China: Information Science, 201
Collaborative Summarization of Topic-Related Videos
Large collections of videos are grouped into clusters by a topic keyword,
such as Eiffel Tower or Surfing, with many important visual concepts repeating
across them. Such a topically close set of videos have mutual influence on each
other, which could be used to summarize one of them by exploiting information
from others in the set. We build on this intuition to develop a novel approach
to extract a summary that simultaneously captures both important
particularities arising in the given video, as well as, generalities identified
from the set of videos. The topic-related videos provide visual context to
identify the important parts of the video being summarized. We achieve this by
developing a collaborative sparse optimization method which can be efficiently
solved by a half-quadratic minimization algorithm. Our work builds upon the
idea of collaborative techniques from information retrieval and natural
language processing, which typically use the attributes of other similar
objects to predict the attribute of a given object. Experiments on two
challenging and diverse datasets well demonstrate the efficacy of our approach
over state-of-the-art methods.Comment: CVPR 201
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