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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: 28 September 2010

Veronica Maidel, Peretz Shoval, Bracha Shapira and Meirav Taieb‐Maimon

The purpose of this paper is to describe a new ontological content‐based filtering method for ranking the relevance of items for readers of news items, and its evaluation…

Abstract

Purpose

The purpose of this paper is to describe a new ontological content‐based filtering method for ranking the relevance of items for readers of news items, and its evaluation. The method has been implemented in ePaper, a personalised electronic newspaper prototype system. The method utilises a hierarchical ontology of news; it considers common and related concepts appearing in a user's profile on the one hand, and in a news item's profile on the other hand, and measures the “hierarchical distances” between these concepts. On that basis it computes the similarity between item and user profiles and rank‐orders the news items according to their relevance to each user.

Design/methodology/approach

The paper evaluates the performance of the filtering method in an experimental setting. Each participant read news items obtained from an electronic newspaper and rated their relevance. Independently, the filtering method is applied to the same items and generated, for each participant, a list of news items ranked according to relevance.

Findings

The results of the evaluations revealed that the filtering algorithm, which takes into consideration hierarchically related concepts, yielded significantly better results than a filtering method that takes only common concepts into consideration. The paper determined a best set of values (weights) of the hierarchical similarity parameters. It also found out that the quality of filtering improves as the number of items used for implicit updates of the profile increases, and that even with implicitly updated profiles, it is better to start with user‐defined profiles.

Originality/value

The proposed content‐based filtering method can be used for filtering not only news items but items from any domain, and not only with a three‐level hierarchical ontology but any‐level ontology, in any language.

Details

Online Information Review, vol. 34 no. 5
Type: Research Article
ISSN: 1468-4527

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Article
Publication date: 1 November 2005

Mohamed Hammami, Youssef Chahir and Liming Chen

Along with the ever growingWeb is the proliferation of objectionable content, such as sex, violence, racism, etc. We need efficient tools for classifying and filtering

Abstract

Along with the ever growingWeb is the proliferation of objectionable content, such as sex, violence, racism, etc. We need efficient tools for classifying and filtering undesirable web content. In this paper, we investigate this problem through WebGuard, our automatic machine learning based pornographic website classification and filtering system. Facing the Internet more and more visual and multimedia as exemplified by pornographic websites, we focus here our attention on the use of skin color related visual content based analysis along with textual and structural content based analysis for improving pornographic website filtering. While the most commercial filtering products on the marketplace are mainly based on textual content‐based analysis such as indicative keywords detection or manually collected black list checking, the originality of our work resides on the addition of structural and visual content‐based analysis to the classical textual content‐based analysis along with several major‐data mining techniques for learning and classifying. Experimented on a testbed of 400 websites including 200 adult sites and 200 non pornographic ones, WebGuard, our Web filtering engine scored a 96.1% classification accuracy rate when only textual and structural content based analysis are used, and 97.4% classification accuracy rate when skin color related visual content based analysis is driven in addition. Further experiments on a black list of 12 311 adult websites manually collected and classified by the French Ministry of Education showed that WebGuard scored 87.82% classification accuracy rate when using only textual and structural content‐based analysis, and 95.62% classification accuracy rate when the visual content‐based analysis is driven in addition. The basic framework of WebGuard can apply to other categorization problems of websites which combine, as most of them do today, textual and visual content.

Details

International Journal of Web Information Systems, vol. 1 no. 4
Type: Research Article
ISSN: 1744-0084

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Article
Publication date: 14 May 2018

Yun-Shan Cheng, Ping-Yu Hsu and Yu-Chin Liu

To retain consumer attention and increase purchasing rates, many e-commerce vendors have adopted content-based recommender systems. However, apart from text-based…

Abstract

Purpose

To retain consumer attention and increase purchasing rates, many e-commerce vendors have adopted content-based recommender systems. However, apart from text-based documents, there is little theoretical background guiding element selection, resulting in a limited content analysis problem. Another inherent problem is overspecialization. The purpose of this paper is to establish a value-based recommendation methodology for identifying favorable attributes, benefits, and values on the basis of means-end chain theory. The identified elements and the relationships between them were utilized to construct a recommender system without incurring either problem.

Design/methodology/approach

This study adopted soft laddering and content analysis to collect popular elements. The relationships between the elements were established by using a hard laddering online questionnaire. The elements and the relationships were utilized to build a hierarchical value map (HVM). A mathematical model was then devised on the basis of the HVM to predict user preferences of attributes.

Findings

The results of a performance comparison showed that the proposed method outperformed the content-based attribute recommendation method and a hybrid method by 39 and 68 percent, respectively.

Originality/value

Although hybrid methods have been proposed to resolve the problem of overspecialization in content-based recommender systems, such methods have incurred “cold start” and “sparsity” problems. The proposed method can provide recommendations without causing these problems while outperforming the content-based and hybrid approaches.

Details

Industrial Management & Data Systems, vol. 118 no. 4
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 2 February 2010

Daniela Godoy, Silvia Schiaffino and Analía Amandi

Recommender agents are used to make recommendations of interesting items in a wide variety of application domains, such as web page recommendation, music, e‐commerce…

Abstract

Purpose

Recommender agents are used to make recommendations of interesting items in a wide variety of application domains, such as web page recommendation, music, e‐commerce, movie recommendation, tourism, restaurant recommendation, among others. Despite the various and different domains in which recommender agents are used and the variety of approaches they use to represent user interests and make recommendations, there is some functionality that is common to all of them, such as user model management and recommendation of interesting items. This paper aims at generalizing these common behaviors into a framework that enables developers to reuse recommender agents' main characteristics in their own developments.

Design/methodology/approach

This work presents a framework for recommendation that provides the control structures, the data structures and a set of algorithms and metrics for different recommendation methods. The proposed framework acts as the base design for recommender agents or applications that want to add the already modeled and implemented capabilities to their own functionality. In contrast with other proposals, this framework is designed to enable the integration of diverse user models, such as demographic, content‐based and item‐based. In addition to the different implementations provided for these components, new algorithms and user model representations can be easily added to the proposed approach. Thus, personal agents originally designed to assist a single user can reuse the behavior implemented in the framework to expand their recommendation strategies.

Findings

The paper describes three different recommender agents built by materializing the proposed framework: a movie recommender agent, a tourism recommender agent, and a web page recommender agent. Each agent uses a different recommendation approach. PersonalSearcher, an agent originally designed to suggest interesting web pages to a user, was extended to collaboratively assist a group of users using content‐based algorithms. MovieRecommender recommends interesting movies using an item‐based approach and Traveller suggests holiday packages using demographic user models. Findings encountered during the development of these agents and their empirical evaluation are described here.

Originality/value

The advantages of the proposed framework are twofold. On the one hand, the functionality provided by the framework enables the development of recommender agents without the need for implementing its whole set of capabilities from scratch. The main processes and data structures of recommender agents are already implemented. On the other hand, already existing agents can be enhanced by incorporating the functionality provided by the recommendation framework in order to act collaboratively.

Details

Internet Research, vol. 20 no. 1
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 5 April 2021

Seungpeel Lee, Honggeun Ji, Jina Kim and Eunil Park

With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer…

Abstract

Purpose

With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions.

Design/methodology/approach

The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used.

Findings

The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%.

Originality/value

This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

Details

The Electronic Library , vol. 39 no. 1
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 1 August 2004

San‐Yih Hwang and Shi‐Min Chuang

In a large‐scale digital library, it is essential to recommend a small number of useful and related articles to users. In this paper, a literature recommendation framework…

Abstract

In a large‐scale digital library, it is essential to recommend a small number of useful and related articles to users. In this paper, a literature recommendation framework for digital libraries is proposed that dynamically provides recommendations to an active user when browsing a new article. This framework extends our previous work that considers only Web usage data by utilizing content information of articles when making recommendations. Methods that make use of pure content data, pure Web usage data, and both content and usage data are developed and compared using the data collected from our university's electronic thesis and dissertation (ETD) system. The experimental results demonstrate that content data and usage data are complements of each other and hybrid methods that take into account of both types of information tend to achieve more accurate recommendations.

Details

Online Information Review, vol. 28 no. 4
Type: Research Article
ISSN: 1468-4527

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Article
Publication date: 11 November 2020

Muzammil Khan, Sarwar Shah Khan, Arshad Ahmad and Arif Ur Rahman

The World Wide Web has become an essential platform for a news publication, and it has become one of the primary sources of information dissemination in the past few…

Abstract

Purpose

The World Wide Web has become an essential platform for a news publication, and it has become one of the primary sources of information dissemination in the past few years. Electronic media, i.e., television channels, magazines and newspapers, have started publishing news online. This online information is prompt to be disappeared because of short life-span and imperative to be archived for the long-term and future generations. This paper presents a content-based similarity measure based on the headings of the news articles for linking digital news stories published in various newspapers during the preservation process that helps to ensure future accessibility.

Design/methodology/approach

To evaluate the accuracy and assess the effectiveness and worth of the proposed measure for linking news articles in Digital News Story Archive (DNSA), we adopted both, system-centric and user-centric (human judgment) evaluation over different datasets of news articles.

Findings

The proposed similarity measure is evaluated using different sizes of datasets, and the results are compared by both user-centric technique, i.e., expert judgment and system-centric techniques, i.e., cosine similarity measure, extended Jaccard measure and common ratio measure for stories (CRMS). The comparison helps to get a broader impact and can be helpful for generalization of the measure for different categories of news articles. Multiple experiments have conducted the findings of which showed that the measure presented viable results for national and international news, while best results for linking sports news articles during preservation based on headings.

Originality/value

The DNSA preserves a huge number of news articles from multiple news sources and to link with a vast collection, which encourages to introduce an efficient linking mechanism with few terms to manipulate. The CRMS is modified to deal with the headings of news articles as a part of the digital news stories preservation framework and comprehensively analysed.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

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Article
Publication date: 1 November 2005

Roberto Baldoni, Roberto Beraldi, Leonardo Querzoni, Gianpaolo Cugola and Matteo Migliavacca

The decoupling and asynchrony properties of the content‐based publish‐subscribe paradigm makes it very appealing for dynamic wireless networks, like those that often occur…

Abstract

The decoupling and asynchrony properties of the content‐based publish‐subscribe paradigm makes it very appealing for dynamic wireless networks, like those that often occur in pervasive computing scenarios. Unfortunately, most of the currently available content‐based publish‐subscribe middleware do not fit the requirements of such extreme scenarios, in which the network is subject to very frequent topological reconfigurations due to mobility of nodes. In this paper we propose a protocol for content‐based message dissemination tailored to Mobile Ad Hoc Networks (MANETs) showing frequent topological changes. Message routing occurs without the support of any network‐wide dispatching infrastructure thus eliminating the need of maintaining such infrastructure on top of a physical network continuously changing its topology. The paper reports an extensive simulation study that confirms the suitability of the proposed approach along with a stochastic analysis of the central mechanism adopted by the protocol.

Details

International Journal of Pervasive Computing and Communications, vol. 1 no. 4
Type: Research Article
ISSN: 1742-7371

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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

Keywords

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