7,181 research outputs found
Latent Dirichlet Allocation (LDA) for improving the topic modeling of the official bulletin of the spanish state (BOE)
Since Internet was born most people can access fully free to a lot sources of information. Every day a lot of web pages are created and new content is uploaded and shared. Never in the history the humans has been more informed but also uninformed due the huge amount of information that can be access. When we are looking for something in any search engine the results are too many for reading and filtering one by one. Recommended Systems (RS) was created to help us to discriminate and filter these information according to ours preferences. This contribution analyses the RS of the official agency of publications in Spain (BOE), which is known as "Mi BOE'. The way this RS works was analysed, and all the meta-data of the published documents were analysed in order to know the coverage of the system. The results of our analysis show that more than 89% of the documents cannot be recommended, because they are not well described at the documentary level, some of their key meta-data are empty. So, this contribution proposes a method to label documents automatically based on Latent Dirichlet Allocation (LDA). The results are that using this approach the system could recommend (at a theoretical point of view) more than twice of documents that it now does, 11% vs 23% after applied this approach
FARS: Fuzzy Ant based Recommender System for Web Users
Recommender systems are useful tools which provide an
adaptive web environment for web users. Nowadays, having a
user friendly website is a big challenge in e-commerce
technology. In this paper, applying the benefits of both
collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on
collaborative behavior of ants (FARS). FARS works in two
phases: modeling and recommendation. First, user’s behaviors
are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations
Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning
An intelligent robot agent based on domain ontology, machine learning
mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning
is presented in this paper. The machine-human co-learning model is established
to help various students learn the mathematical concepts based on their
learning ability and performance. Meanwhile, the robot acts as a teacher's
assistant to co-learn with children in the class. The FML-based knowledge base
and rule base are embedded in the robot so that the teachers can get feedback
from the robot on whether students make progress or not. Next, we inferred
students' learning performance based on learning content's difficulty and
students' ability, concentration level, as well as teamwork sprit in the class.
Experimental results show that learning with the robot is helpful for
disadvantaged and below-basic children. Moreover, the accuracy of the
intelligent FML-based agent for student learning is increased after machine
learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie
Hybrid Recommender Systems: A Systematic Literature Review
Recommender systems are software tools used to generate and provide suggestions for items
and other entities to the users by exploiting various strategies. Hybrid recommender systems
combine two or more recommendation strategies in different ways to benefit from their complementary
advantages. This systematic literature review presents the state of the art in hybrid
recommender systems of the last decade. It is the first quantitative review work completely focused
in hybrid recommenders. We address the most relevant problems considered and present
the associated data mining and recommendation techniques used to overcome them. We also
explore the hybridization classes each hybrid recommender belongs to, the application domains,
the evaluation process and proposed future research directions. Based on our findings, most of
the studies combine collaborative filtering with another technique often in a weighted way. Also
cold-start and data sparsity are the two traditional and top problems being addressed in 23 and
22 studies each, while movies and movie datasets are still widely used by most of the authors.
As most of the studies are evaluated by comparisons with similar methods using accuracy metrics,
providing more credible and user oriented evaluations remains a typical challenge. Besides
this, newer challenges were also identified such as responding to the variation of user context,
evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid
recommenders represent a good basis with which to respond accordingly by exploring newer
opportunities such as contextualizing recommendations, involving parallel hybrid algorithms,
processing larger datasets, etc
An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things
5G edge computing enabled Internet of Medical Things (IoMT) is an efficient
technology to provide decentralized medical services while Device-to-device
(D2D) communication is a promising paradigm for future 5G networks. To assure
secure and reliable communication in 5G edge computing and D2D enabled IoMT
systems, this paper presents an intelligent trust cloud management method.
Firstly, an active training mechanism is proposed to construct the standard
trust clouds. Secondly, individual trust clouds of the IoMT devices can be
established through fuzzy trust inferring and recommending. Thirdly, a trust
classification scheme is proposed to determine whether an IoMT device is
malicious. Finally, a trust cloud update mechanism is presented to make the
proposed trust management method adaptive and intelligent under an open
wireless medium. Simulation results demonstrate that the proposed method can
effectively address the trust uncertainty issue and improve the detection
accuracy of malicious devices
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