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Journal ArticleDOI

Ontology-based library recommender system using MapReduce

01 Mar 2015-Cluster Computing (Springer US)-Vol. 18, Iss: 1, pp 113-121
TL;DR: The results of this extensive performance study show that the proposed algorithm can scale recommender systems for all-pairs similarity searching, and this paper details the development and employment of the MapReduce framework.
Abstract: 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.
Citations
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Journal ArticleDOI
TL;DR: An automatized method for retrieving the subjective Internet users information and creating ontologies is described, making it possible to automatically create knowledge databases using the common knowledge of a large amount of people.

39 citations


Cites methods from "Ontology-based library recommender ..."

  • ...In [19], a library recommendation system that uses big data techniques like MapReduce and ontologies is presented....

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Journal ArticleDOI
TL;DR: This paper proposes a collaborative filtering recommendation approach that filters out programming problems suitable for students’ programming skills using an enriched user-problem matrix that implies a better student role representation, facilitating the computation of closer neighborhoods and hence a more accurate recommendation.
Abstract: 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.

35 citations

Journal ArticleDOI
TL;DR: 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 and features the ability to connect to hospital healthcare service centers using the IoT devices installed in vehicles.
Abstract: 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.

24 citations


Cites background from "Ontology-based library recommender ..."

  • ...Phunchongharn Technique conducted a study regarding electromagneticwave interference inwireless LAN (WLAN, Wireless local area network) environments for E-healthcare applications in hospitals [1, 2]....

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  • ...Internet of Things based healthcare services refer to those customized services that are provided utilizing IoT platforms and wearable devices [1,4,15]....

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Journal ArticleDOI
TL;DR: A knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation model-based recommendation technique and a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain.
Abstract: 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...

21 citations

Journal ArticleDOI
TL;DR: A recommender system to aggregate author information from multiple publisher networks is proposed that evaluates the trustworthiness of the author recommendations based on the impact of the authors’ contributions and the recency and popularity of their work as well as the correlations among these factors.
Abstract: Recommender systems have roots in numerous fields, and their use is widespread in the modern world. The scientific community is striving to enhance the quality of life by breaking innovative barriers and developing solutions that had never previously been considered. In an ideal world, an individual researcher would participate in various fields of research and make cumulative impactful contributions to benefit society. However, in reality, this goal is difficult to attain without a team of collaborators. Collaboration refers to the information of partnerships that bring uniquely talented researchers together around a common idea. However, efforts to seek such co-authors not only are challenging but also occasionally yield no significant results. In this paper, we propose a recommender system to aggregate author information from multiple publisher networks. It evaluates the trustworthiness of the author recommendations based on the impact of the authors’ contributions and the recency and popularity of their work as well as the correlations among these factors. On this basis, the system generates a list of prospective collaborators who might be of interest to a given researcher.

11 citations


Cites methods from "Ontology-based library recommender ..."

  • ...MapReduce brute force algorithm has been introduced in [14] to enhance the collaborative filtering for libraries with a large number of users to avoid similarity computation problem....

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References
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Journal ArticleDOI
Jeffrey Dean1, Sanjay Ghemawat1
06 Dec 2004
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Abstract: MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.

20,309 citations

Journal ArticleDOI
Jeffrey Dean1, Sanjay Ghemawat1
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Abstract: MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

17,663 citations

Journal ArticleDOI
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

9,873 citations

Proceedings ArticleDOI
01 Apr 2001
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Abstract: Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

8,634 citations


"Ontology-based library recommender ..." refers background in this paper

  • ...Like-minded users exhibit similar profiles; thus, the recommender system reviews their previous book rating to calculate the top-rated books, suggesting these to users [8,9]....

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Journal ArticleDOI
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Abstract: Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

5,686 citations


"Ontology-based library recommender ..." refers background in this paper

  • ...Copious time is spent measuring the similarity for the library recommender system between users or items to determine likemined users and potential items of interest [24]....

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