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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…

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

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Article
Publication date: 7 March 2016

Yajun Leng, Qing Lu and Changyong Liang

Collaborative recommender systems play a crucial role in providing personalized services to online consumers. Most online shopping sites and many other applications now…

Abstract

Purpose

Collaborative recommender systems play a crucial role in providing personalized services to online consumers. Most online shopping sites and many other applications now use the collaborative recommender systems. The measurement of the similarity plays a fundamental role in collaborative recommender systems. Some of the most well-known similarity measures are: Pearson’s correlation coefficient, cosine similarity and mean squared differences. However, due to data sparsity, accuracy of the above similarity measures decreases, which makes the formation of inaccurate neighborhood, thereby resulting in poor recommendations. The purpose of this paper is to propose a novel similarity measure based on potential field.

Design/methodology/approach

The proposed approach constructs a dense matrix: user-user potential matrix, and uses this matrix to compute potential similarities between users. Then the potential similarities are modified based on users’ preliminary neighborhoods, and k users with the highest modified similarity values are selected as the active user’s nearest neighbors. Compared to the rating matrix, the potential matrix is much denser. Thus, the sparsity problem can be efficiently alleviated. The similarity modification scheme considers the number of common neighbors of two users, which can further improve the accuracy of similarity computation.

Findings

Experimental results show that the proposed approach is superior to the traditional similarity measures.

Originality/value

The research highlights of this paper are as follows: the authors construct a dense matrix: user-user potential matrix, and use this matrix to compute potential similarities between users; the potential similarities are modified based on users’ preliminary neighborhoods, and k users with the highest modified similarity values are selected as the active user’s nearest neighbors; and the proposed approach performs better than the traditional similarity measures. The manuscript will be of particular interests to the scientists interested in recommender systems research as well as to readers interested in solution of related complex practical engineering problems.

Details

Kybernetes, vol. 45 no. 3
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 13 January 2021

Manish Sinha and Divyank Srivastava

With the current pandemic situation, the world is shifting to online buying and therefore the purpose of this study is to understand how the industry can improve sales…

Abstract

Purpose

With the current pandemic situation, the world is shifting to online buying and therefore the purpose of this study is to understand how the industry can improve sales based on the product recommendations shown on their online platforms.

Design/methodology/approach

This paper has studied content-based filtering using decision trees algorithm and collaborative filtering using K-nearest neighbour algorithm and measured their impact on sales of product of different genres on e-commerce websites and if their recommendation causes a difference in sales.This paper has conducted a field experiment to analyse the customer frequency, change in sales caused by different algorithms and also tried analysing the change in buying preferences of customers in post-pandemic situation and how this paper can improve on the search results by incorporating them in the already used algorithms.

Findings

This study indicates that different algorithms cause differences in sales and score over each other depending upon the category of the product sold. It also suggests that post-Covid, the buying frequency and the preferences of consumers have changed significantly.

Research limitations/implications

The study is limited to existing users of these sites, it also requires the sites to have a huge database of active users and products. Also, the preferences and likings of Indian subcontinent might not generally apply everywhere else.

Originality/value

This study enables better insight into consumer behaviour, thus enabling the data scientists to design better algorithms and help the companies improve their product sales.

Details

International Journal of Innovation Science, vol. 13 no. 2
Type: Research Article
ISSN: 1757-2223

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Book part
Publication date: 24 November 2010

Chirag Shah

Collaboration is often required for activities that are too complex or difficult to be dealt with by a single individual. Many situations requiring information-seeking…

Abstract

Collaboration is often required for activities that are too complex or difficult to be dealt with by a single individual. Many situations requiring information-seeking activities also call for people to work together. Often the methods, systems, and tools that provide access to information assume that they are used only by individuals working on their tasks alone. This review points to the need to acknowledge the importance of collaboration in information-seeking processes, to study models, and to develop systems that are specifically designed to enable collaborative information seeking (CIS) tasks. This chapter reviews the literature from various domains including library and information science, human–computer interaction, collaborative systems, and information retrieval. Focus of the review is on the extent to which people work together on information seeking tasks and the systems and tools that are available for them to be successful. Since CIS occurs in the broader context of collaboration in general, a review of literature about collaborations is first undertaken to define it and place it into context with related terms such as cooperation and communication. A more focused review of research follows relating CIS to systems that have attempted to support such interactions. Included are identification and synthesis of a number of core issues in the field and how best to evaluate systems and collaborative tools. Key lessons learned from the review are summarized, and gaps in the literature identified to spur future research and study.

Details

Advances in Librarianship
Type: Book
ISBN: 978-1-84950-979-4

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Article
Publication date: 23 September 2013

Behnam Taraghi, Martin Grossegger, Martin Ebner and Andreas Holzinger

The use of articles from scientific journals is an important part of research-based teaching at universities. The selection of relevant work from among the increasing…

Abstract

Purpose

The use of articles from scientific journals is an important part of research-based teaching at universities. The selection of relevant work from among the increasing amount of scientific literature can be problematic; the challenge is to find relevant recommendations, especially when the related articles are not obviously linked. This paper seeks to discuss these issues.

Design/methodology/approach

This paper focuses on the analysis of user activity traces in journals using the open source software “Open Journal Systems” (OJS). The research questions to what extent end users follow a certain link structure given within OJS or immediately select the articles according to their interests. In the latter case, the recorded data sets are used for creating further recommendations. The analysis is based on an article matrix, displaying the usage frequency of articles and their user selected successive articles within the OJS. Furthermore, the navigation paths are analysed.

Findings

It was found that the users tend to follow a set navigation structure. Moreover, a hybrid recommendation system for OJS is described, which uses content based filtering as the basic system extended by the results of a collaborative filtering approach.

Originality/value

The paper presents two original contributions: the analysis of user path tracing and a novel algorithm that allows smooth integration of new articles into the existing recommendations, due to the fact that scientific journals are published in a frequent and regular time sequence.

Details

Online Information Review, vol. 37 no. 5
Type: Research Article
ISSN: 1468-4527

Keywords

Content available
Article
Publication date: 21 June 2019

Jiemin Zhong, Haoran Xie and Fu Lee Wang

A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a…

Abstract

Purpose

A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.

Design/methodology/approach

The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.

Findings

The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.

Originality/value

The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

Details

Asian Association of Open Universities Journal, vol. 14 no. 1
Type: Research Article
ISSN: 2414-6994

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Book part
Publication date: 10 February 2012

Manuel Burghardt, Markus Heckner and Christian Wolff

Purpose — This chapter illustrates and explains the ambiguity and vagueness of the term social search and aims at describing and classifying the heterogeneous landscape of…

Abstract

Purpose — This chapter illustrates and explains the ambiguity and vagueness of the term social search and aims at describing and classifying the heterogeneous landscape of social search implementations on the WWW.

Methodology/approach — We have looked at different definitions as well as the context of social search by carrying out an extensive literature review, and tried to unify and enhance existing ideas and concepts. Our definition of social search is illustrated by a general review of existing social search engines, which are analyzed and described by their specific features and social aspects.

Findings — The chapter presents a discussion of social search as well as a comparison of existing social search engines.

Social implications — The definition of social search and the comparison of social search engines summarize the many ways people can search the web together and allow for an assessment of future developments in this area.

Originality/value of paper — Although different attempts to define social search have been made in the past, we present an argumentation that unifies some existing definitions and which is different from other interpretations of the social search concept. We present an overview and a comparison of the different genres of social search engines.

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Article
Publication date: 27 July 2010

Hassan Naderi and Beatrice Rumpler

This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information…

Abstract

Purpose

This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems.

Design/methodology/approach

A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user‐centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system.

Findings

The results show that among the proposed UPSC formulas in this paper, the (query‐document)‐graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems.

Research limitations/implications

This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information.

Originality/value

The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.

Details

Journal of Documentation, vol. 66 no. 4
Type: Research Article
ISSN: 0022-0418

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Article
Publication date: 1 May 1999

Murat Karamuftuoglu

The main objective of this article is to show the increasing relevance of the knowledge production capability of information storage and retrieval systems in the context…

Abstract

The main objective of this article is to show the increasing relevance of the knowledge production capability of information storage and retrieval systems in the context of ‘perpetual innovation’, otherwise known as the ‘information’ economy. The knowledge production potential of information retrieval systems is only barely recognised in the information science community. Traditionally, information professionals and retrieval systems devised by them are conceived merely as guardians and facilitators of knowledge. This prevents information professionals playing a key role in an innovation based economy. In a perpetual innovation economy, information/knowledge embedded in commodities becomes the main source of profit. However, the peculiar character of information/knowledge means that privately owned knowledge tends to flow back into the public domain. This peculiarity necessitates continuous production of new knowledge applied to products and production techniques. Creative acts are not individualistic but collective/collaborative processes. Emerging collaborative systems on computer networks, such as the Internet, make it possible to devise work environments that are conducive to the development and cultivation of collective practices. Informational retrieval systems designers and practitioners may find it useful to study such systems to develop retrieval mechanisms that enhance creativity and facilitate knowledge production as well as knowledge transfer. It is hoped that by putting information retrieval in the context of the perpetual innovation economy, the knowledge production potential of information retrieval systems becomes more widely acknowledged and accepted among information practitioners.

Details

Aslib Proceedings, vol. 51 no. 4
Type: Research Article
ISSN: 0001-253X

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Article
Publication date: 17 March 2020

Hossein Dehdarirad, Javad Ghazimirsaeid and Ammar Jalalimanesh

The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR…

Abstract

Purpose

The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper.

Design/methodology/approach

To identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions.

Findings

A total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions.

Originality/value

Given that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.

Details

Data Technologies and Applications, vol. 54 no. 2
Type: Research Article
ISSN: 2514-9288

Keywords

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