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Article
Publication date: 8 October 2020

Diana Floegel

This paper examines promotional practices Netflix employs via Twitter and its automated recommendation system in order to deepen our understanding of how streaming…

Abstract

Purpose

This paper examines promotional practices Netflix employs via Twitter and its automated recommendation system in order to deepen our understanding of how streaming services contribute to sociotechnical inequities under capitalism.

Design/methodology/approach

Tweets from two Netflix Twitter accounts as well as material features of Netflix's recommendation system were qualitatively analyzed using inductive analysis and the constant comparative method in order to explore dimensions of Netflix's promotional practices.

Findings

Twitter accounts and the recommendation system profit off people's labor to promote content, and such labor allows Netflix to create and refine classification practices wherein both people and content are categorized in inequitable ways. Labor and classification feed into Netflix's production of culture via appropriation on Twitter and algorithmic decision-making within both the recommendation system and broader AI-driven production practices.

Social implications

Assemblages that include algorithmic recommendation systems are imbued with structural inequities and therefore unable to be fixed by merely diversifying cultural industries or retooling algorithms on streaming platforms. It is necessary to understand systemic injustices within these systems so that we may imagine and enact just alternatives.

Originality/value

Findings demonstrate that via surveillance tactics that exploit people's labor for promotional gains, enforce normative classification schemes, and culminate in normative cultural productions, Netflix engenders practices that regulate bodies and culture in ways that exemplify interconnections between people, machines, and social institutions. These interconnections further reflect and result in material inequities that crystalize within sociotechnical processes.

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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

Keywords

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Article
Publication date: 5 February 2018

Chengxin Yin, Yan Guo, Jianguo Yang and Xiaoting Ren

The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.

Abstract

Purpose

The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.

Design/methodology/approach

By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.

Findings

The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.

Originality/value

Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

Details

Industrial Management & Data Systems, vol. 118 no. 1
Type: Research Article
ISSN: 0263-5577

Keywords

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Article
Publication date: 18 April 2017

Yuto Ishida, Takahiro Uchiya and Ichi Takumi

In recent years, e-commerce (EC) sites dealing in various goods and services have increased along with internet popularity. Now, very few EC recommendation systems present…

Abstract

Purpose

In recent years, e-commerce (EC) sites dealing in various goods and services have increased along with internet popularity. Now, very few EC recommendation systems present a concrete reason for their recommendations. Therefore, because user preferences strongly influence outcomes, evaluation and selection are difficult for items, such as books, movies and luxury goods. The purpose of this paper is evoking interest by showing the review as a reason for a user’s decision-making factor. This paper aims to presents the development and introduction of a recommendation system that presents a review adapted to user preference.

Design/methodology/approach

The system presents a review to the user, which indicates the reason for matching the item contents and user preferences. Thereby, this system enables the creation of personalized reasons for recommendations.

Findings

Recommendation sentences conforming to user preferences are effective for item selection. Even with a simple method, in this paper, it was possible to present a review which is an item selection factor sufficient for the user.

Originality/value

This system can show a recommendation sentence that conforms to a user’s preferences merely from a user profile with the tag data of a product. This paper dealt in movies, but it can easily be applied even for other items.

Details

International Journal of Web Information Systems, vol. 13 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

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Article
Publication date: 8 June 2010

Jie Lu, Qusai Shambour, Yisi Xu, Qing Lin and Guangquan Zhang

The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e‐services, in particular, business…

Abstract

Purpose

The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e‐services, in particular, business partner recommendation e‐services for Australian small to medium enterprises (SMEs).

Design/methodology/approach

The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item‐based collaborative filtering (CF) similarity and item‐based semantic similarity techniques. This hybrid approach is implemented into an intelligent business‐partner‐locator recommendationsystem prototype called BizSeeker.

Findings

The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets.

Practical implications

The study would be of great value in e‐government personalization research. It would facilitate the transformation of the current G2B e‐services into a new stage wherein the e‐government agencies offer personalized e‐services to business users. The study would help government policy decision‐makers to increase the adoption of e‐government services.

Originality/value

Providing personalized e‐services by e‐government can be seen as an evolution of the intentions‐based approach and will be one of the next directions of government e‐services. This paper develops a new recommender approach and systems to improve personalization of government e‐services.

Details

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

Keywords

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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…

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.

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Article
Publication date: 8 June 2010

Francisco J. Martínez‐López, Inma Rodríguez‐Ardura, Juan Carlos Gázquez‐Abad, Manuel J. Sánchez‐Franco and Claudia C. Cabal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web…

Abstract

Purpose

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved.

Design/methodology/approach

The approach takes the form of theoretical modelling.

Findings

A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified.

Research limitations/implications

The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research.

Practical implications

The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented.

Originality/value

The paper is based on and adapts classical theories of consumer behaviour, integrating them into particular theories developed within the framework of computer‐mediated environments.

Details

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

Keywords

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Article
Publication date: 13 March 2017

Yan Guo, Minxi Wang and Xin Li

The purpose of this paper is to make the mobile e-commerce shopping more convenient and avoid information overload by a mobile e-commerce recommendation system using an…

Abstract

Purpose

The purpose of this paper is to make the mobile e-commerce shopping more convenient and avoid information overload by a mobile e-commerce recommendation system using an improved Apriori algorithm.

Design/methodology/approach

Combined with the characteristics of the mobile e-commerce, an improved Apriori algorithm was proposed and applied to the recommendation system. This paper makes products that are recommended to consumers valuable by improving the data mining efficiency. Finally, a Taobao online dress shop is used as an example to prove the effectiveness of an improved Apriori algorithm in the mobile e-commerce recommendation system.

Findings

The results of the experimental study clearly show that the mobile e-commerce recommendation system based on an improved Apriori algorithm increases the efficiency of data mining to achieve the unity of real time and recommendation accuracy.

Originality/value

The improved Apriori algorithm is applied in the mobile e-commerce recommendation system solving the limitation of the visual interface in a mobile terminal and the mass data that are continuously generated. The proposed recommendation system provides greater prediction accuracy than conventional systems in data mining.

Details

Industrial Management & Data Systems, vol. 117 no. 2
Type: Research Article
ISSN: 0263-5577

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

Keywords

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Article
Publication date: 15 June 2012

Andrea Barraza‐Urbina and Angela Carrillo Ramos

The purpose of this paper is to describe UWIRS (Ubiquitous Web Information Retrieval Solution), an agent‐based Web Information Retrieval (WIR) solution designed taking…

Abstract

Purpose

The purpose of this paper is to describe UWIRS (Ubiquitous Web Information Retrieval Solution), an agent‐based Web Information Retrieval (WIR) solution designed taking into account the unique features of the World Wide Web (WWW) and the limitations of existing WIR solutions for ubiquitous environments.

Design/methodology/approach

UWIRS can offer recommendation services by using the Multi‐Agent Vizier Recommendation Framework (Vizier). Vizier was designed under a generic approach and therefore can provide services to information retrieval applications so these may offer product recommendations that consider several adaptation/personalization dimensions (e.g. user dimension, context, among others).

Findings

Overall, the main challenge resides on: location, retrieval, integration and presentation of information from the WWW, quickly and accurately, to satisfy a user's singular information needs.

Originality/value

In UWIRS, agents cooperate in order to retrieve personalized information, considering user needs, goals, preferences and contextual features. UWIRS's agents are responsible for: interpreting user input and adding adaptation information by means of a query enrichment process; identifying and selecting the appropriate data sources taking into consideration the Profile Set (composed of User, Device and Information‐Provider Profiles); executing query routing and the information retrieval process; integrating and filtering the retrieved results; and lastly, coherent presentation of quality and relevant ubiquitous information (anytime, anywhere and anyhow) that satisfies the user's particular information needs and constraints associated to his/her access device.

Details

International Journal of Web Information Systems, vol. 8 no. 2
Type: Research Article
ISSN: 1744-0084

Keywords

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