41,418 research outputs found
Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
Recommender systems (RSs) provide an effective way of alleviating the
information overload problem by selecting personalized items for different
users. Latent factors based collaborative filtering (CF) has become the popular
approaches for RSs due to its accuracy and scalability. Recently, online social
networks and user-generated content provide diverse sources for recommendation
beyond ratings. Although {\em social matrix factorization} (Social MF) and {\em
topic matrix factorization} (Topic MF) successfully exploit social relations
and item reviews, respectively, both of them ignore some useful information. In
this paper, we investigate the effective data fusion by combining the
aforementioned approaches. First, we propose a novel model {\em \mbox{MR3}} to
jointly model three sources of information (i.e., ratings, item reviews, and
social relations) effectively for rating prediction by aligning the latent
factors and hidden topics. Second, we incorporate the implicit feedback from
ratings into the proposed model to enhance its capability and to demonstrate
its flexibility. We achieve more accurate rating prediction on real-life
datasets over various state-of-the-art methods. Furthermore, we measure the
contribution from each of the three data sources and the impact of implicit
feedback from ratings, followed by the sensitivity analysis of hyperparameters.
Empirical studies demonstrate the effectiveness and efficacy of our proposed
model and its extension.Comment: 27 pages, 11 figures, 6 tables, ACM TKDD 201
Human Aspects and Perception of Privacy in Relation to Personalization
The concept of privacy is inherently intertwined with human attitudes and
behaviours, as most computer systems are primarily designed for human use.
Especially in the case of Recommender Systems, which feed on information
provided by individuals, their efficacy critically depends on whether or not
information is externalized, and if it is, how much of this information
contributes positively to their performance and accuracy. In this paper, we
discuss the impact of several factors on users' information disclosure
behaviours and privacy-related attitudes, and how users of recommender systems
can be nudged into making better privacy decisions for themselves. Apart from
that, we also address the problem of privacy adaptation, i.e. effectively
tailoring Recommender Systems by gaining a deeper understanding of people's
cognitive decision-making process.Comment: Seminar on Privacy and Big Data, Summer Semester 2017, Informatik 5,
RWTH Aachen University, German
Back To The Future: On Predicting User Uptime
Correlation in user connectivity patterns is generally considered a problem
for system designers, since it results in peaks of demand and also in the
scarcity of resources for peer-to-peer applications. The other side of the coin
is that these connectivity patterns are often predictable and that, to some
extent, they can be dealt with proactively.
In this work, we build predictors aiming to determine the probability that
any given user will be online at any given time in the future. We evaluate the
quality of these predictors on various large traces from instant messaging and
file sharing applications.
We also illustrate how availability prediction can be applied to enhance the
behavior of peer-to-peer applications: we show through simulation how data
availability is substantially increased in a distributed hash table simply by
adjusting data placement policies according to peer availability prediction and
without requiring any additional storage from any peer
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
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
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
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
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
Interface and Data Biopolitics in the Age of Hyperconnectivity
This article describes their biopolitical implications for design from
psychological, cultural, legal, functional and aesthetic/perceptive ways, in
the framework of Hyperconnectivity: the condition according to which
person-to-person, person-to-machine and machine-to-machine communication
progressively shift to networked and digital means. A definition is given for
the terms of "interface biopolitics" and "data biopolitics", as well as
evidence supporting these definitions and a description of the technological,
theoretical and practice-based innovations bringing them into meaningful
existence. Interfaces, algorithms, artificial intelligences of various types,
the tendency in quantified self and the concept of "information bubbles" will
be examined in terms of interface and data biopolitics, from the point of view
of design, and for their implications in terms of freedoms, transparency,
justice and accessibility to human rights. A working hypothesis is described
for technologically relevant design practices and education processes, in order
to confront with these issues in critical, ethical and inclusive ways.Comment: in Proceedings of Design For Next, 12th EAD Conference, Sapienza
University Rome, 12-14 April 201
Tracking Large-Scale Video Remix in Real-World Events
Social information networks, such as YouTube, contains traces of both
explicit online interaction (such as "like", leaving a comment, or subscribing
to video feed), and latent interactions (such as quoting, or remixing parts of
a video). We propose visual memes, or frequently re-posted short video
segments, for tracking such latent video interactions at scale. Visual memes
are extracted by scalable detection algorithms that we develop, with high
accuracy. We further augment visual memes with text, via a statistical model of
latent topics. We model content interactions on YouTube with visual memes,
defining several measures of influence and building predictive models for meme
popularity. Experiments are carried out on with over 2 million video shots from
more than 40,000 videos on two prominent news events in 2009: the election in
Iran and the swine flu epidemic. In these two events, a high percentage of
videos contain remixed content, and it is apparent that traditional news media
and citizen journalists have different roles in disseminating remixed content.
We perform two quantitative evaluations for annotating visual memes and
predicting their popularity. The joint statistical model of visual memes and
words outperform a concurrence model, and the average error is ~2% for
predicting meme volume and ~17% for their lifespan.Comment: 11 pages, accepted for journal publicatio
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