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Article
Publication date: 3 June 2019

Bilal Hawashin, Shadi Alzubi, Tarek Kanan and Ayman Mansour

This paper aims to propose a new efficient semantic recommender method for Arabic content.

Abstract

Purpose

This paper aims to propose a new efficient semantic recommender method for Arabic content.

Design/methodology/approach

Three semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature.

Findings

Experiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time.

Originality/value

Although many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.

Article
Publication date: 27 July 2021

Louisa Ha, Mohammad Hatim Abuljadail, Claire Youngnyo Joa and Kisun Kim

This study aims to examine the difference between personalized and non-personalized recommendations in influencing YouTube users’ video choices. In addition, whether men…

Abstract

Purpose

This study aims to examine the difference between personalized and non-personalized recommendations in influencing YouTube users’ video choices. In addition, whether men and women have a significant difference in using recommendations was compared and the predictors of recommendation video use frequency were explored.

Design/methodology/approach

A survey of 524 Saudi Arabia college students was conducted using computer-assisted self-administered interviews to collect their video recommendation sources and how likely they follow the recommendation from different sources.

Findings

Video links posted on social media used by the digital natives were found as the most effective form of recommendation shows that social approval is important in influencing trials. Recommendations can succeed in both personalized and non-personalized ways. Personalized recommendations as in YouTube recommended videos are almost the same as friends and family’s non-personalized posting of video links on social media in convincing people to watch the videos. Contrary to expectations, Saudi men college students are more likely to use recommendations than women students.

Research limitations/implications

The use of a non-probability sample is a major limitation and self-reported frequency may result in over- or under-estimation of video use.

Practical implications

Marketers will realize that they may not need the personalized recommendation from the large site. They can use social media recommendations by the consumers’ friends and family. E-mail is the worst platform for a recommendation.

Social implications

Recommendation is a credible source and can overcome the avoidance of advertising. Its influence on consumers will be increasing in years to come with the algorithmic recommendation and social media use.

Originality/value

This is the first study to compare the influence of different online recommendation sources and compare personalized and non-personalized recommendations. As recommendation is growing more and more important with algorithm development online, the study results have high reference values to marketers in Islamic countries and beyond.

Details

Journal of Islamic Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1759-0833

Keywords

Article
Publication date: 11 July 2019

Manjula Wijewickrema, Vivien Petras and Naomal Dias

The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two…

Abstract

Purpose

The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject corpora (covering the social sciences and medicine). The study examines the appropriateness of three text similarity measures and the impact of numerous aspects of corpus documents on system performance.

Design/methodology/approach

Implemented three similarity measures one at a time on a journal recommender system with two separate journal corpora. Two distinct samples of test abstracts were classified and evaluated based on the normalized discounted cumulative gain.

Findings

The BM25 similarity measure outperforms both the cosine and unigram language similarity measures overall. The unigram language measure shows the lowest performance. The performance results are significantly different between each pair of similarity measures, while the BM25 and cosine similarity measures are moderately correlated. The cosine similarity achieves better performance for subjects with higher density of technical vocabulary and shorter corpus documents. Moreover, increasing the number of corpus journals in the domain of social sciences achieved better performance for cosine similarity and BM25.

Originality/value

This is the first work related to comparing the suitability of a number of string-based similarity measures with distinct corpora for journal recommender systems.

Details

The Electronic Library , vol. 37 no. 3
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 1 June 2010

Ulrich Herb, Eva Kranz, Tobias Leidinger and Björn Mittelsdorf

Usually the impact of research and researchers is quantified by using citation data: either by journal‐centered citation data as in the case of the journal impact factor…

Abstract

Purpose

Usually the impact of research and researchers is quantified by using citation data: either by journal‐centered citation data as in the case of the journal impact factor (JIF) or by author‐centered citation data as in the case of the Hirsch‐ or h‐index. This paper aims to discuss a range of impact measures, especially usage‐based metrics, and to report the results of two surveys.

Design/methodology/approach

The first part of the article analyzes both citation‐based and usage‐based metrics. The second part is based on the findings of the surveys: one in the form of a brainstorming session with information professionals and scientists at the OAI6 conference in Geneva, the second in the form of expert interviews, mainly with scientists.

Findings

The results of the surveys indicate an interest in the social aspects of science, like visualizations of social graphs both for persons and their publications. Furthermore, usage data are considered an appropriate measure to describe quality and coverage of scientific documents; admittedly, the consistence of usage information among repositories has to be kept in mind. The scientists who took part in the survey also asked for community services, assuming these might help to identify relevant scientific information more easily. Some of the other topics of interest were personalization or easy submission procedures.

Originality/value

This paper delineates current discussions about citation‐based and usage‐based metrics. Based on the results of the surveys, it depicts which functionalities could enhance repositories, what features are required by scientists and information professionals, and whether usage‐based services are considered valuable. These results also outline some elements of future repository research.

Details

OCLC Systems & Services: International digital library perspectives, vol. 26 no. 2
Type: Research Article
ISSN: 1065-075X

Keywords

Content available
Article
Publication date: 5 June 2009

343

Abstract

Details

Library Hi Tech News, vol. 26 no. 5/6
Type: Research Article
ISSN: 0741-9058

Article
Publication date: 22 September 2020

Arghya Ray, Pradip Kumar Bala and Rashmi Jain

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both…

Abstract

Purpose

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data.

Design/methodology/approach

This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes.

Findings

Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively.

Practical implications

Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts.

Originality/value

The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.

Details

Benchmarking: An International Journal, vol. 28 no. 2
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 21 October 2019

Priyadarshini R., Latha Tamilselvan and Rajendran N.

The purpose of this paper is to propose a fourfold semantic similarity that results in more accuracy compared to the existing literature. The change detection in the URL…

Abstract

Purpose

The purpose of this paper is to propose a fourfold semantic similarity that results in more accuracy compared to the existing literature. The change detection in the URL and the recommendation of the source documents is facilitated by means of a framework in which the fourfold semantic similarity is implied. The latest trends in technology emerge with the continuous growth of resources on the collaborative web. This interactive and collaborative web pretense big challenges in recent technologies like cloud and big data.

Design/methodology/approach

The enormous growth of resources should be accessed in a more efficient manner, and this requires clustering and classification techniques. The resources on the web are described in a more meaningful manner.

Findings

It can be descripted in the form of metadata that is constituted by resource description framework (RDF). Fourfold similarity is proposed compared to three-fold similarity proposed in the existing literature. The fourfold similarity includes the semantic annotation based on the named entity recognition in the user interface, domain-based concept matching and improvised score-based classification of domain-based concept matching based on ontology, sequence-based word sensing algorithm and RDF-based updating of triples. The aggregation of all these similarity measures including the components such as semantic user interface, semantic clustering, and sequence-based classification and semantic recommendation system with RDF updating in change detection.

Research limitations/implications

The existing work suggests that linking resources semantically increases the retrieving and searching ability. Previous literature shows that keywords can be used to retrieve linked information from the article to determine the similarity between the documents using semantic analysis.

Practical implications

These traditional systems also lack in scalability and efficiency issues. The proposed study is to design a model that pulls and prioritizes knowledge-based content from the Hadoop distributed framework. This study also proposes the Hadoop-based pruning system and recommendation system.

Social implications

The pruning system gives an alert about the dynamic changes in the article (virtual document). The changes in the document are automatically updated in the RDF document. This helps in semantic matching and retrieval of the most relevant source with the virtual document.

Originality/value

The recommendation and detection of changes in the blogs are performed semantically using n-triples and automated data structures. User-focussed and choice-based crawling that is proposed in this system also assists the collaborative filtering. Consecutively collaborative filtering recommends the user focussed source documents. The entire clustering and retrieval system is deployed in multi-node Hadoop in the Amazon AWS environment and graphs are plotted and analyzed.

Details

International Journal of Intelligent Unmanned Systems, vol. 7 no. 4
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 4 September 2009

Michael Schuricht, Zachary Davis, Michael Hu, Shreyas Prasad, Peter M. Melliar‐Smith and Louise E. Moser

Mobile handheld devices, such as cellular phones and personal digital assistants, are inherently small and lack an intuitive and natural user interface. Speech recognition…

Abstract

Purpose

Mobile handheld devices, such as cellular phones and personal digital assistants, are inherently small and lack an intuitive and natural user interface. Speech recognition and synthesis technology can be used in mobile handheld devices to improve the user experience. The purpose of this paper is to describe a prototype system that supports multiple speech‐enabled applications in a mobile handheld device.

Design/methodology/approach

The main component of the system, the Program Manager, coordinates and controls the speech‐enabled applications. Human speech requests to, and responses from, these applications are processed in the mobile handheld device, to achieve the goal of human‐like interactions between the human and the device. In addition to speech, the system also supports graphics and text, i.e., multimodal input and output, for greater usability, flexibility, adaptivity, accuracy, and robustness. The paper presents a qualitative and quantitative evaluation of the prototype system. The Program Manager is currently designed to handle the specific speech‐enabled applications that we developed.

Findings

The paper determines that many human interactions involve not single applications but multiple applications working together in possibly unanticipated ways.

Research limitations/implications

Future work includes generalization of the Program Manager so that it supports arbitrary applications and the addition of new applications dynamically. Future work also includes deployment of the Program Manager and the applications on cellular phones running the Android Platform or the Openmoko Framework.

Originality/value

This paper presents a first step towards a future human interface for mobile handheld devices and for speech‐enabled applications operating on those devices.

Details

International Journal of Pervasive Computing and Communications, vol. 5 no. 3
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 27 May 2022

Izhar Oplatka and Alaa Afif Elmalak-Watted

The aim of the current study was to explore emotional closeness and emotional distance between Arab teachers who teach in the Jewish State Educational System and their…

Abstract

Purpose

The aim of the current study was to explore emotional closeness and emotional distance between Arab teachers who teach in the Jewish State Educational System and their Jewish counterparts in the school.

Design/methodology/approach

The research used semi-structured interviews with 16 Arab and Jewish teachers in Israel.

Findings

The authors identified patterns of emotional closeness and emotional distance among Arab and Jewish teachers, perception gaps among Jewish and Arab teachers and the factors affecting emotional closeness/distance among them. Empirical and practical implications are suggested.

Originality/value

The study sheds light on the emotional aspects of multicultural educational teams and workplaces and increases our understanding of the complexity of teacher emotion in multi-ethnic and multi-religious staffrooms.

Details

Journal of Professional Capital and Community, vol. 7 no. 3
Type: Research Article
ISSN: 2056-9548

Keywords

Article
Publication date: 10 May 2022

Arghya Ray, Pradip Kumar Bala, Nripendra P. Rana and Yogesh K. Dwivedi

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services…

Abstract

Purpose

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

Design/methodology/approach

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Findings

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

Originality/value

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

1 – 10 of 30