12,867 research outputs found
Predicting the popularity of Web 2.0 items based on user comments
National Research Foundation (NRF) Singapore under its International Research Centre @ Singapore Funding Initiativ
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1
Finding Influential Users in Social Media Using Association Rule Learning
Influential users play an important role in online social networks since
users tend to have an impact on one other. Therefore, the proposed work
analyzes users and their behavior in order to identify influential users and
predict user participation. Normally, the success of a social media site is
dependent on the activity level of the participating users. For both online
social networking sites and individual users, it is of interest to find out if
a topic will be interesting or not. In this article, we propose association
learning to detect relationships between users. In order to verify the
findings, several experiments were executed based on social network analysis,
in which the most influential users identified from association rule learning
were compared to the results from Degree Centrality and Page Rank Centrality.
The results clearly indicate that it is possible to identify the most
influential users using association rule learning. In addition, the results
also indicate a lower execution time compared to state-of-the-art methods
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
This paper contributes improvements on both the effectiveness and efficiency
of Matrix Factorization (MF) methods for implicit feedback. We highlight two
critical issues of existing works. First, due to the large space of unobserved
feedback, most existing works resort to assign a uniform weight to the missing
data to reduce computational complexity. However, such a uniform assumption is
invalid in real-world settings. Second, most methods are also designed in an
offline setting and fail to keep up with the dynamic nature of online data. We
address the above two issues in learning MF models from implicit feedback. We
first propose to weight the missing data based on item popularity, which is
more effective and flexible than the uniform-weight assumption. However, such a
non-uniform weighting poses efficiency challenge in learning the model. To
address this, we specifically design a new learning algorithm based on the
element-wise Alternating Least Squares (eALS) technique, for efficiently
optimizing a MF model with variably-weighted missing data. We exploit this
efficiency to then seamlessly devise an incremental update strategy that
instantly refreshes a MF model given new feedback. Through comprehensive
experiments on two public datasets in both offline and online protocols, we
show that our eALS method consistently outperforms state-of-the-art implicit MF
methods. Our implementation is available at
https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Online knowledge repositories typically rely on their users or dedicated
editors to evaluate the reliability of their content. These evaluations can be
viewed as noisy measurements of both information reliability and information
source trustworthiness. Can we leverage these noisy evaluations, often biased,
to distill a robust, unbiased and interpretable measure of both notions?
In this paper, we argue that the temporal traces left by these noisy
evaluations give cues on the reliability of the information and the
trustworthiness of the sources. Then, we propose a temporal point process
modeling framework that links these temporal traces to robust, unbiased and
interpretable notions of information reliability and source trustworthiness.
Furthermore, we develop an efficient convex optimization procedure to learn the
parameters of the model from historical traces. Experiments on real-world data
gathered from Wikipedia and Stack Overflow show that our modeling framework
accurately predicts evaluation events, provides an interpretable measure of
information reliability and source trustworthiness, and yields interesting
insights about real-world events.Comment: Accepted at 26th World Wide Web conference (WWW-17
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