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1 – 10 of 126
Article
Publication date: 27 December 2021

Fatemehalsadat Afsahhosseini and Yaseen Al-Mulla

The purpose of this study is to identify the knowledge gap and future opportunities for developing mobile recommender system in tourism sector that lead to comfortable, targeted…

Abstract

Purpose

The purpose of this study is to identify the knowledge gap and future opportunities for developing mobile recommender system in tourism sector that lead to comfortable, targeted and attractive tourism. A recommender system improves the traditional classification algorithms and has incorporated many advanced machine learning algorithms.

Design/methodology/approach

Design of this application followed a smart, hybrid and context-aware recommender system, which includes various recommender systems. With the recommender system's help, useful management for time and budget is obtained for tourists, since they usually have financial and time constraints for selecting the point of interests (POIs) and so more purposeful trip planned with decreased traffic and air pollution.

Findings

The finding of this research showed that the inclusion of additional information about the item, user, circumstances, objects or conditions and the environment could significantly impact recommendation quality and information and communications technology has become one part of the tourism value chain.

Practical implications

The application consists of (1) registration: with/without social media accounts, (2) user information: country, gender, age and his/her specific interests, (3) context data: available time, alert, price, spend time, weather, location, transportation.

Social implications

The study’s social implications include connecting the app and registration through social media to a more social relationship, with its textual reviews, or user review as user-generated content for increasing accuracy.

Originality/value

The originality of this research work lies on introducing a new content- and knowledge-based algorithm for POI recommendations. An “Alert” context emphasizing on safety, supplies and essential infrastructure is considered as a novel context for this application.

Details

Journal of Cultural Heritage Management and Sustainable Development, vol. 13 no. 4
Type: Research Article
ISSN: 2044-1266

Keywords

Article
Publication date: 1 January 2024

Shahrzad Yaghtin and Joel Mero

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other…

Abstract

Purpose

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

Design/methodology/approach

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

Findings

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

Originality/value

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

Details

Journal of Business & Industrial Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0885-8624

Keywords

Open Access
Article
Publication date: 28 July 2020

Julián Monsalve-Pulido, Jose Aguilar, Edwin Montoya and Camilo Salazar

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently…

1792

Abstract

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

Details

Applied Computing and Informatics, vol. 20 no. 1/2
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 31 March 2023

Chia-Ling Chang, Yen-Liang Chen and Jia-Shin Li

The purpose of this paper is to provide a cross-platform recommendation system that recommends the most suitable public Instagram accounts to Facebook users.

Abstract

Purpose

The purpose of this paper is to provide a cross-platform recommendation system that recommends the most suitable public Instagram accounts to Facebook users.

Design/methodology/approach

We collect data from both Facebook and Instagram and then propose a similarity matching mechanism for recommending the most appropriate Instagram accounts to Facebook users. By removing the data disparity between the two heterogeneous platforms and integrating them, the system is able to make more accurate recommendations.

Findings

The results show that the method proposed in this paper can recommend suitable public Instagram accounts to Facebook users with very high accuracy.

Originality/value

To the best of the authors’ knowledge, this is the first study to propose a recommender system to recommend Instagram public accounts to Facebook users. Second, our proposed method can integrate heterogeneous data from two different platforms to generate collaborative recommendations. Furthermore, our cross-platform system reveals an innovative concept of how multiple platforms can promote their respective platforms in a unified, cooperative and collaborative manner.

Details

The Electronic Library , vol. 41 no. 2/3
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 1 May 2023

Jiaxin Ye, Huixiang Xiong, Jinpeng Guo and Xuan Meng

The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of…

Abstract

Purpose

The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web.

Design/methodology/approach

The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model to classify the reviews by function. The frequency characteristics of the reviews are mined by word frequency analysis, and the relationship between reviews and total book score is mined by correlation analysis. Then, the reviews are classified into three roles: celebrity, general and passerby. Finally, the authors can form user groups, mine group features and combine group features with book fine-grained ratings to make book group recommendations.

Findings

Overall, the best recommendations are made by Synopsis comments, with the accuracy, recall, F-value and Hellinger distance of 52.9%, 60.0%, 56.3% and 0.163, respectively. The F1 index of the recommendations based on the author and the writing comments is improved by 2.5% and 0.4%, respectively, compared to the Synopsis comments.

Originality/value

Previous studies on book recommendation often recommend relevant books for users by mining the similarity between books, so the set of book recommendations recommended to users, especially to groups, always focuses on the few types. The proposed method can effectively ensure the diversity of recommendations by mining users’ tendency to different review attributes of books and recommending books for the groups. In addition, this study also investigates which types of reviews should be used to make book recommendations when targeting groups with specific tendencies.

Details

The Electronic Library , vol. 41 no. 2/3
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 28 February 2023

V. Senthil Kumaran and R. Latha

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

Abstract

Purpose

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

Design/methodology/approach

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

Findings

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

Research limitations/implications

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

Practical implications

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

Originality/value

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

Article
Publication date: 23 June 2023

Rubel, Bijay Prasad Kushwaha and Md Helal Miah

This study aims to highlight the inconsistency between conventional knowledge push judgements and the price of knowledge push. Also, a three-way decision-based relevant knowledge…

Abstract

Purpose

This study aims to highlight the inconsistency between conventional knowledge push judgements and the price of knowledge push. Also, a three-way decision-based relevant knowledge push algorithm was proposed.

Design/methodology/approach

Using a ratio of 80–20%, the experiment randomly splits the data into a training set and a test set. Each video is used as a knowledge unit (structure) in the research, and the category is used as a knowledge attribute. The limit is then determined using the user’s overall rating. To calculate the pertinent information obtained through experiments, the fusion coefficient is needed. The impact of the push model is then examined in comparison to the conventional push model. In the experiment, relevant knowledge is compared using three push models, two push models based on conventional International classification functioning (ICF), and three push models based on traditional ICF. The average push cost accuracy rate, recall rate and coverage rate are metrics used to assess the push effect.

Findings

The three-way knowledge push models perform better on average than the other push models in this research in terms of push cost, accuracy rate and recall rate. However, the three-way knowledge push models suggested in this study have a lower coverage rate than the two-way push model. So three-way knowledge push models condense the knowledge push and forfeit a particular coverage rate. As a result, improving knowledge results in higher accuracy rates and lower push costs.

Practical implications

This research has practical ramifications for the quick expansion of knowledge and its hegemonic status in value creation as the main methodology for knowledge services.

Originality/value

To the best of the authors’ knowledge, this is the first theory developed on the three-way decision-making process of knowledge push services to increase organizational effectiveness and efficiency.

Details

VINE Journal of Information and Knowledge Management Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5891

Keywords

Article
Publication date: 14 October 2021

Mona Bokharaei Nia, Mohammadali Afshar Kazemi, Changiz Valmohammadi and Ghanbar Abbaspour

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right…

Abstract

Purpose

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

Design/methodology/approach

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

Findings

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

Research limitations/implications

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

Practical implications

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

Originality/value

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

Article
Publication date: 6 February 2024

Junyi Chen, Buqing Cao, Zhenlian Peng, Ziming Xie, Shanpeng Liu and Qian Peng

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application…

Abstract

Purpose

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

Design/methodology/approach

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Findings

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

Originality/value

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Details

International Journal of Web Information Systems, vol. 20 no. 2
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 30 August 2023

Donghui Yang, Yan Wang, Zhaoyang Shi and Huimin Wang

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and…

Abstract

Purpose

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

Design/methodology/approach

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

Findings

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

Originality/value

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

Details

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

Keywords

1 – 10 of 126