https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1961_creatingknowledgedatabases.pdf

Creating knowledge databases for storing and sharing people knowledge automatically using group decision making and fuzzy ontologies

The use of programming online judges (POJ) to support students acquiring programming skills is common nowadays because this type of software contains a large collection of programming exercises to be solved by students. A POJ not only provides exercises but also automates the code compilation and its evaluation process. A common problem that students face when using POJ is information overload, as choosing the right problem to solve can be quite frustrating due to the large number of problems offered. The integration of current POJs into e-learning systems such as Intelligent Tutoring Systems (ITSs) is hard because of the lack of necessary information in ITSs. Hence, the aim of this paper is to support students with the information overload problem by using a collaborative filtering recommendation approach that filters out programming problems suitable for students’ programming skills. It uses an enriched user-problem matrix that implies a better student role representation, facilitating the computation of closer neighborhoods and hence a more accurate recommendation. Additionally a novel data preprocessing step that manages anomalous users’ behaviors that could affect the recommendation generation is also integrated in the recommendation process. A case study is carried out on a POJ real dataset showing that the proposal outperforms other previous approaches.

A recommendation approach for programming online judges supported by data preprocessing techniques

As IoT technologies have become more available, healthcare patients increasingly want to be provided with services at places other than hospitals or their homes. Most patients with implantable devices still visit hospitals, sometimes using a self-driving car or public transportation to obtain services. When an emergency situation develops for a patient in a vehicle lacking the means to address the crisis, the patient’s life cannot help but be in danger. The present paper proposes an IoT healthcare service model that will enable patients with a medical sensor to be provided healthcare services in a vehicle installed with IoT devices. To solve problems in existing models that do not include electromagnetic interference-based (EMI) multiple property management and control, the proposed model involves medical sensors with different multiple-property information guarantee targeted SINRs and minimum blackouts. The model also features the ability to connect to hospital healthcare service centers using the IoT devices installed in vehicles, thereby enabling information on the patient’s condition and first aid needs to be transmitted in real time. To secure the patient’s biometric data during information transmission, the proposed model weights that information to enhance the efficiency of the IoT devices. Performance evaluation results revealed that compared to existing algorithms, the communication strength of the proposed model is an average of 5.2% higher, and network efficiency between IoT devices and medical sensors is an average of 7.6% higher. In addition, the overhead on IoT devices was an average of 3.5% lower.

An IoT healthcare service model of a vehicle using implantable devices

In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves hi...

Towards a knowledge-based probabilistic and context-aware social recommender system:

Distributed collaborative filtering with singular ratings for large scale recommendation

Recommender systems have been proven useful in numerous contemporary applications and helping users effectively identify items of interest within massive and potentially overwhelming collections. Among the recommender system techniques, the collaborative filtering mechanism is the most successful; it leverages the similar tastes of similar users, which can serve as references for recommendation. However, a major weakness for the collaborative filtering mechanism is its performance in computing the pairwise similarity of users. Thus, the MapReduce framework was examined as a potential means to address this performance problem. This paper details the development and employment of the MapReduce framework, examining whether it improves the performance of a personal ontology based recommender system in a digital library. The results of this extensive performance study show that the proposed algorithm can scale recommender systems for all-pairs similarity searching.

Ontology-based library recommender system using MapReduce

Recommender system can help users to effectively identify interested items from a potentially overwhelming huge collection of items, and it has been shown to be very useful in many e-commerce applications. Collaborative filtering (CF), which assumes that similar users may have similar tastes, is one of the most widely used Recommender system techniques. However, one of the major weaknesses for the CF mechanism is the computational cost in computing pairwise similarity of users. This paper attempts to tackle the computational problem of all pairs similarity using the MapReduce technique in the Hadoop framework. We give an overview of our development on using a parallel filtering algorithm to improve the performance of a personal ontology based recommender system for digital library. The experimental results show that the proposed algorithm can indeed scale out the recommender systems for all pairs search.

Ping-Jen Kuo

Papers

Ontology-based library recommender system using MapReduce

Scaling Out Recommender System for Digital Libraries with MapReduce