با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Accurate Content Push for Content-Centric Social Networks: A Big Data Support Online Learning Approach
وارد کردن محتوای دقیق برای شبکه های محتوا محور: رویکرد یادگیری آنلاین با پشتیبانی داده های بزرگ-2018 With the rapid growth of the social network, information overload becomes a critical issue. Service providers push a
lot of redundant contents and advertisements to users every day.
Thus, users’ interests and the probability of reading them have
dropped considerably and the network load is wasted. To address
this issue, accurate content push is needed, where the main challenges are proving precise descriptions of users and supporting the
big data nature of users and contents. Content-centric networking
(CCN) has emerged as a new network architecture to meet today’s
requirement for content access and delivery. By using the named
content, CCN makes it possible to track users’ real-time interests
and motivates us studying a novel content accurate push (or called
content recommendation) system. In this paper, we model this issue as a novel contextual multiarmed bandit based Monte Carlo tree
search problem and propose a big data support online learning algorithm to meet the demand of content push with low cost. To avoid
destroying CCN’s energy efficient feature, the energy consumption
is considered into our module. Then, we theoretically prove that
our online learning algorithm achieves sublinear regret bound and
sublinear storage, which is very efficient in the big data context
and do not increase the network burden. Experiments in an offline
collected dataset show that our approach significantly increases
the accuracy and convergence speed against other state-of-the-art
bandit algorithms and can overcome the cold start problem as well
Index Terms: Online learning, content-centric networking, big data, social network, recommender system, contextual bandit, monte-carlo tree search. |
مقاله انگلیسی |