Abstract: Mass media sources, specifically the news
media, have traditionally informed us of daily events. In modern times, social
media services such as Twitter provide an enormous amount of user-generated
data, which have great potential to contain informative news-related content.
For these resources to be useful, we must find a way to filter noise and only
capture the content that, based on its similarity to the news media, is
considered valuable. However, even after noise is removed, information overload
may still exist in the remaining data—hence, it is convenient to prioritize it
for consumption. To achieve prioritization, information must be ranked in order
of estimated importance considering three factors. First, the temporal
prevalence of a particular topic in the news media is a factor of importance,
and can be considered the media focus (MF) of a topic. Second, the temporal
prevalence of the topic in social media indicates its user attention (UA).
Last, the interaction between the social media users who mention this topic indicates
the strength of the community discussing it, and can be regarded as the user
interaction (UI) toward the topic. We propose an unsupervised
framework—SociRank—which identifies news topics prevalent in both social media
and the news media, and then ranks them by relevance using their degrees of MF,
UA, and UI. Our experiments
IEEE 2017: RAPARE: A Generic Strategy for Cold-Start Rating Prediction
Problem
Abstract:I n recent years, recommender system is one of
indispensable components in many e-commerce websites. One of the major
challenges that largely remains open is the cold-start problem, which can be
viewed as a barrier that keeps the cold-start users/items away from the
existing ones. In this paper, we aim to break through this barrier for
cold-start users/items by the assistance of existing ones. In particular,
inspired by the classic Elo Rating System, which has been widely adopted in
chess tournaments; we propose a novel rating comparison strategy (RAPARE) to
learn the latent profiles of cold-start users/items. The center-piece of our
RAPARE is to provide a fine-grained calibration on the latent profiles of
cold-start users/items by exploring the differences between cold-start and
existing users/items. As a generic strategy, our proposed strategy can be
instantiated into existing methods in recommender systems. To reveal the
capability of RAPARE strategy, we instantiate our strategy on two prevalent
methods in recommender systems, i.e., the matrix factorization based and
neighborhood based collaborative filtering.
IEEE 2017:
l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items
Abstract: We develop a novel framework, named as
l-injection, to address the sparsity problem of recommender systems. By
carefully injecting low values to a selected set of unrated user-item pairs in
a user-item matrix, we demonstrate that top-N recommendation accuracies of
various collaborative filtering (CF) techniques can be significantly and
consistently improved. We first adopt the notion of pre-use preferences of
users toward a vast amount of unrated items. Using this notion, we identify
uninteresting items that have not been rated yet but are likely to receive low
ratings from users, and selectively impute them as low values. As our proposed
approach is method-agnostic, it can be easily applied to a variety of CF
algorithms. Through comprehensive experiments with three real-life datasets
(e.g., Movielens, Ciao, and Watcha), we demonstrate that our solution
consistently and universally enhances the accuracies of existing CF algorithms
(e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore,
our solution improves the running time of those CF methods by 1.2 to 2.3 times
when its setting produces the best accuracy.
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IEEE 2017: Vehicular
Cloud Data Collection for Intelligent Transportation Systems
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IEEE 2017: Optimizing Green
Energy, Cost, and Availability in Distributed Data Centers
Abstract: Integrating renewable energy and ensuring high availability
are among two major requirements for geodistributed data centers. Availability
is ensured by provisioning spare capacity across the data centers to mask data
center failures (either partial or complete). We propose a mixed integer linear
programming formulation for capacity planning while minimizing the total cost
of ownership (TCO) for highly available, green, distributed data centers. We
minimize the cost due to power consumption and server deployment, while
targeting a minimum usage of green energy. Solving our model shows that
capacity provisioning considering green energy integration, not only lowers
carbon footprint but also reduces the TCO. Results show that up to 40% green
energy usage is feasible with marginal increase in the TCO compared to the
other cost-aware models.
