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This study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations.

In this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004–2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies.

The outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers’ challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers; (2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques); (3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context; (4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques; (5) performing a SWOT analysis on the selected studies; (6) based on the developer’s challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context.

This study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can overcome the identified challenges (including mashup-oriented API, Time-consuming process, learn how to use the API, integrated problem, API method usage location and limited usage of code) in the future by proposing a framework or recommendation system. Furthermore, the classification of current state-of-the-art API recommendation techniques also helps the researchers who wish to work in the future in the context of API recommendation.

This study not only facilitates the researcher but also facilitates the practitioners in several ways. The current study guides the developer in minimizing the development time in terms of selecting relevant APIs rather than following traditional manual selection. Moreover, this study facilitates integrating APIs in a project. Thus, the recommendation system saves the time for developers, and increases their productivity.

API recommendation remains an active area of research in web and mobile-based applications development. The authors believe that this study acts as a useful tool for the interested researchers and practitioners as it will contribute to the body of knowledge in API recommendations context.

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

Online advertisement brings huge revenue to many websites. There are many types of online advertisement; this paper aims to focus on the online banner ads which are usually placed in a particular news website. The investigated news website adopts a pay-per-ad payment model, where the advertisers are charged when they rent a banner from the website during a particular period. In this payment model, the website needs to ensure that the ad pushed frequency of each ad on the banner is similar. Under such advertisement push rules, an ad-recommendation mechanism considering ad push fairness is required.

The authors proposed a novel ad recommendation method that considers both ad-push fairness and personal interests. The authors take every ad’s exposure time into consideration and investigate users’ three different usage experiences in the website to identify the main factors affecting the interests of users. Online ad recommendation is conducted on the investigated news website.

The results of the experiments show that the proposed approach performs better than the traditional approach. This method can not only enhance the average click rate of all ads in the website but also ensure reasonable fairness of exposure frequency of each ad. The online experiment results demonstrate the effectiveness of this approach.

Existing researches had not considered both the advertisement recommendation and ad-push fairness together. With the proposed novel ad recommendation model, the authors can improve the ad click-through rate of ads with reasonable push fairness. The website provider can thereby increase the commercial value of advertising and user satisfaction.

This paper aims to use the concept of machine learning to enable people and machines to interact more certainly to extend and expand human expertise and cognition.

Intelligent code reuse recommendations based on code big data analysis, mining and learning can effectively improve the efficiency and quality of software reuse, including common code units in a specific field and common code units that are not related to the field.

Focusing on the topic of context-based intelligent code reuse recommendation, this paper expounds the research work in two aspects mainly in practical applications of smart decision support and cognitive adaptive systems: code reuse recommendation based on template mining and code reuse recommendation based on deep learning.

On this basis, the future development direction of intelligent code reuse recommendation based on context has prospected.

This paper aims to describe the usage of a hybrid weightage-based recommender system focused on books and implementing it at an industrial level, using various recommendation approaches. Additionally, it focuses on integrating the model into the most widely used platform application.

It is an industrial level implementation of a recommendation system by applying different recommendation approaches. This study describes the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application.

This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the hybridized system outperforms over other existing recommender system.

The proposed recommendation system is an industrial level implementation of a recommendation system by applying different recommendation approaches. The recommendation system is centralized to books and its recommendation. In this paper, the authors also describe the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application. This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the newly created hybridized system outperforms the Netflix recommendation model as well as the Hybrid book recommendation system model as has been clearly shown in the Results Analysis section of the book. The source-code can be available at https://github.com/debajyoty/recomender-system.git.

The purpose of this study is to investigate a practical strategy for integrating application programming interfaces (APIs) and standard interchange protocols (SIPs) within library and information services. This study will seek to determine how such an integration strategy can improve access to resources, enhance the user experience, optimize library operations and improve the overall efficiency of library services.

A qualitative approach to research will be used in this study. This study will be based on the review of relevant literature sources, case studies and real examples. The data analyzes to determine the practical application of SIP and API integration and identify the major methods, approaches and processes used by libraries to successfully implement integration projects.

This study explores that library and information services may achieve numerous benefits from API and SIP integration. The cases describe how libraries have managed to improve access, user experience, operational efficiency and general performance. Libraries have integrated APIs and SIP to create seamless search experiences, establish communication networks in real-time, and develop automated workflows and customer services. API and SIP integration will transform libraries in future.

The originality of this study is the focus of the API and SIP integration. While other authors have discussed the concept of integration from a theoretical standpoint, this study presents practical recommendations and implementation advice for librarians and researchers. This study uses real cases and examples to illustrate how libraries today have managed to improve their operations with the help of APIs and SIP integration.

Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

This paper aims to introduce a machine learning-based “My Account” recommender for implementation in open discovery environments such as VuFind among others.

The approach to implementing machine learning-based personalized recommenders is undertaken as applied research leveraging data streams of transactional checkout data from discovery systems.

The authors discuss the need for large data sets from which to build an algorithm and introduce a prototype recommender service, describing the prototype’s data flow pipeline and machine learning processes.

The browse paradigm of discovery has neglected to leverage discovery system data to inform the development of personalized recommendations; with this paper, the authors show novel approaches to providing enhanced browse functionality by way of a user account.

In the age of big data and machine learning, advances in deep learning technology and data stream processing make it possible to leverage discovery system data to inform the development of personalized recommendations.

This paper seeks to suggest a model for location‐based recommendation services that enable greater access to print and electronic resources.

The paper takes the form of a synthesis of previous work in basic and applied collections‐based wayfinding incorporating library and information science (LIS) literature on user context and system recommendations.

The paper identifies problems that will need to be solved before implementation of the production‐level recommendation service and suggests possible implications the system may have on reference and instruction services.

The paper provides computing workflows necessary to implement a library recommendation service based on user location. iPhone Software Developer Kit templates are leveraged for modeling data and interface prototypes. Use cases and user models are developed.

This study examines the cultural history of the Api-Opi deity in Opi, Nsukka, Enugu State of Nigeria. The study sets out to examine the re-emergence of youthful worshippers of Api-Opi, despite the penetration of Christianity in the area.

The study employed ethnographic observation and field visits to the shrine of Api-Opi in Opi community of Enugu State, Nigeria. In addition, this study uncovers new information drawn from semi-structured interview questions undertaken in the study area between March and October of 2019.

Against certain claims on the impact of Christianity on Africa's traditional religions, the study found that the Api-Opi deity has withstood these post-colonial changes, growing its followership, particularly amongst the youths. It demonstrated the resilience of Igbo Traditional Worship System even in the midst of culture clash and religious iconoclasm advanced by Christianity in Igboland, Nigeria.

Evidence from this study helps debunk the notions of Eurocentric scholars who say African traditional religions are fetish, barbaric or primitive. It also shows how indigenous communities have protected and preserved their religious heritage despite the wave of modernization and other eternal influences. The study contributes to the increasing conversations about the role of traditional religion in the cultural resilience/revitalization of indigenous communities.