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Article
Publication date: 3 December 2018

Xue Yang

Social recommender systems have recently gained increasing popularity. The purpose of this paper is to investigate the influences of informational factors on purchase…

Abstract

Purpose

Social recommender systems have recently gained increasing popularity. The purpose of this paper is to investigate the influences of informational factors on purchase intention in social recommender systems.

Design/methodology/approach

Specifically, this study validated the mediating effect of trust in recommendations and the perceived value between informational factors and consumers’ purchase intention.

Findings

The results confirm that recommendation persuasiveness was a strong predictor of trust in recommendations and perceived value. Recommendation completeness was positively related to trust in recommendations and perceived value as well. Trust in recommendations and perceived value was found to be strong drivers of purchase intention.

Originality/value

The author identifies two sets of informational factors, i.e. recommendation persuasiveness and recommendation completeness, which are relevant to consumer attitudes. The current study proved that informational factors on consumers’ purchase intention are fully mediated through trust in recommendations and perceived value.

Details

Online Information Review, vol. 44 no. 2
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 15 March 2018

Fatemeh Alyari and Nima Jafari Navimipour

This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about…

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Abstract

Purpose

This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. To achieve this aim, the authors use systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected recommender systems and its main techniques, as well as their benefits and drawbacks in general.

Design/methodology/approach

In this paper, the SLR method is utilized with the aim of identifying, evaluating and integrating the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. Also, the authors discussed recommender system and its techniques in general without a specific domain.

Findings

The major developments in categories of recommender systems are reviewed, and new challenges are outlined. Furthermore, insights on the identification of open issues and guidelines for future research are provided. Also, this paper presents the systematical analysis of the recommender system literature from 2005. The authors identified 536 papers, which were reduced to 51 primary studies through the paper selection process.

Originality/value

This survey will directly support academics and practical professionals in their understanding of developments in recommender systems and its techniques.

Details

Kybernetes, vol. 47 no. 5
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 6 August 2019

Karzan Wakil, Fatemeh Alyari, Mahdi Ghasvari, Zahra Lesani and Lila Rajabion

This paper aims to propose a new method for evaluating the success of the recommender systems based on customer history, product classification and prices criteria in the…

Abstract

Purpose

This paper aims to propose a new method for evaluating the success of the recommender systems based on customer history, product classification and prices criteria in the electronic commerce. To evaluate the validity of the model, the structural equation modeling technique is employed.

Design/methodology/approach

A method has been suggested to evaluate the impact of customer history, product classification and prices on the success of the recommender systems in electronic commerce. After that, the authors investigated the relationship between these factors. To achieve this goal, the structural equation modeling technique was used for statistical conclusion validity. The results of gathered data from employees of a company in Iran is indicated the impact of the customer history on the success of recommender systems in e-commerce which is related with the user profile, expert opinion, neighbors, loyalty and clickstream. These factors positively influence the success of recommender systems in ecommerce.

Findings

The obtained results demonstrated the efficiency and effectiveness of the proposed model in term of the success of the recommender systems in the electronic commerce.

Originality/value

In this paper, the effective factors of success of recommender systems in electronic commerce are pointed out and the approach to increase the efficiency of this system is applied into a practical example.

Details

Kybernetes, vol. 49 no. 5
Type: Research Article
ISSN: 0368-492X

Keywords

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…

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. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-1266

Keywords

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: 2 March 2012

Simon Wakeling, Paul Clough, Barbara Sen and Lynn Silipigni Connaway

Moves towards more interactive services on the web have led libraries to add an increasing range of functionality to their OPACS. Given the prevalence of recommender

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Abstract

Purpose

Moves towards more interactive services on the web have led libraries to add an increasing range of functionality to their OPACS. Given the prevalence of recommender systems on the wider web, especially in e‐commerce environments, this paper aims to review current research in this area that is of particular relevance to the library community. It attempts to gauge the uptake of recommender systems in exiting OPAC services, and identify issues that might be responsible for inhibiting wider uptake.

Design/methodology/approach

This paper draws on an extensive literature review, as well as original research comparing the functionality of 211 public and 118 university library OPACs in the UK. Examining current recommender systems research, it outlines the most significant recommendation models and reviews research in two key areas of recommender systems design: data acquisition, and the explanation of recommendations. It discusses three existing library recommendation systems: BibTip, LibraryThing for Libraries and the in‐house system at the University of Huddersfield.

Findings

The authors' analysis indicates that the incorporation of recommender systems into library services is extremely low, with only 2 per cent of public libraries and 11 per cent of university libraries in the UK offering the feature. While system limitations and budget constraints are perhaps partly to blame, it is suggested that library professionals have perhaps yet to be persuaded that the value of recommendations to library users is great enough to warrant their inclusion becoming a priority.

Originality/value

This paper represents the first study of UK library OPACs to focus on the prevalence of recommender systems.

Article
Publication date: 19 April 2018

Aleksandar Simović

With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user…

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Abstract

Purpose

With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations.

Design/methodology/approach

This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions.

Findings

As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction.

Social implications

The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization.

Originality/value

A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.

Details

Library Hi Tech, vol. 36 no. 3
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 8 June 2010

Rosa M. Rodríguez, Macarena Espinilla, Pedro J. Sánchez and Luis Martínez‐López

Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid…

Abstract

Purpose

Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid recommender system which uses a knowledge‐based recommendation model to provide good cold start recommendations.

Design/methodology/approach

Hybridizing a collaborative system and a knowledge‐based system, which uses incomplete preference relations means that the cold start problem is solved. The management of customers' preferences, necessities and perceptions implies uncertainty. To manage such an uncertainty, this information has been modeled by means of the fuzzy linguistic approach.

Findings

The use of linguistic information provides flexibility, usability and facilitates the management of uncertainty in the computation of recommendations, and the use of incomplete preference relations in knowledge‐based recommender systems improves the performance in those situations when collaborative models do not work properly.

Research limitations/implications

Collaborative recommender systems have been successfully applied in many situations, but when the information is scarce such systems do not provide good recommendations.

Practical implications

A linguistic hybrid recommendation model to solve the cold start problem and provide good recommendations in any situation is presented and then applied to a recommender system for restaurants.

Originality/value

Current recommender systems have limitations in providing successful recommendations mainly related to information scarcity, such as the cold start. The use of incomplete preference relations can improve these limitations, providing successful results in such situations.

Details

Internet Research, vol. 20 no. 3
Type: Research Article
ISSN: 1066-2243

Keywords

Article
Publication date: 6 February 2017

Fan Wu, Ya-Han Hu and Ping-Rong Wang

Most academic libraries provide book recommendation services to enable readers to recommend books to the libraries. To facilitate decision-making in book acquisition, this…

Abstract

Purpose

Most academic libraries provide book recommendation services to enable readers to recommend books to the libraries. To facilitate decision-making in book acquisition, this study aimed to develop a method to determine the ranking of the recommended books based on the recommender network.

Design/methodology/approach

The recommender network was conducted to establish relationships among book recommenders and their similar readers by using circulation records. Furthermore, social computing techniques were used to evaluate the degree of representativeness of the recommenders and subsequently applied as a criterion to rank the recommended books. Empirical studies were performed to demonstrate the effectiveness of the proposed ranking system. The Spearman’s correlation coefficients between the proposed ranking system and the ranking obtained using reader circulation statistics were used as performance measure.

Findings

The ranking calculated using the proposed ranking mechanism was highly and moderately correlated to the ranking obtained using reader circulation statistics. The ranking of recommended books by the librarians was moderately and poorly correlated to the ranking calculated using reader circulation statistics.

Practical implications

The book recommender can be used to improve the accuracy of book recommendations.

Originality/value

This study is the first that considers the recommender network on library book acquisition. The results also show that the proposed ranking mechanism can facilitate effective book-acquisition decisions in libraries.

Details

The Electronic Library, vol. 35 no. 1
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 5 January 2022

Imen Gmach, Nadia Abaoub, Rubina Khan, Naoufel Mahfoudh and Amira Kaddour

In this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety…

Abstract

Purpose

In this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.

Design/methodology/approach

Methodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.

Findings

The purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.

Originality/value

The authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

Details

Technological Sustainability, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2754-1312

Keywords

1 – 10 of over 1000