Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.     

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.     

Intelligent tourism recommender systems: A survey

Recommender systems are currently being applied in many different domains. This paper focuses on their application in tourism. A comprehensive and thorough search of the smart e-Tourism recommenders reported in the Artificial Intelligence journals and conferences since 2008 has been made. The paper provides a detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques. The survey also provides some guidelines for the construction of tourism recommenders and outlines the most promising areas of work in the field for the next years.     

A survey of book recommender systems

The act of reading has benefits for individuals and societies, yet studies show that reading declines, especially among the young. Recommender systems can help stop such decline. We present a survey of recommender systems in the domain of books. We have categorized the systems into six classes, and highlighted the main trends, issues, evaluation approaches and datasets. Other research areas, such as psychology, are consulted to understand users’ books choices and reading models.     

Expert systems in production planning and scheduling: A state-of-the-art survey

Intelligent solutions, based on expert systems, to solve problems in the field of production planning and scheduling are becoming more and more widespread nowadays. Especially the last decade has witnessed a growing number of manufacturing companies, including glass, oil, aerospace, computers, electronics, metal and chemical industries—to name just a few—interested in the applications of expert systems (ESs) in manufacturing. This paper is a state-of-the-art review of the use of ESs in the field of production planning and scheduling. The paper presents famous expert systems known in the literature and current applications, analyzes the relative benefits and concludes by sharing thoughts and estimations on ESs future prospects in this area.     

Artificial compound eye: a survey of the state-of-the-art

An artificial compound eye system is the bionic system of natural compound eyes with much wider field-of-view, better capacity to detect moving objects and higher sensitivity to light intensity than ordinary single-aperture eyes. In recent years, renewed attention has been paid to the artificial compound eyes, due to their better characteristics inheriting from insect compound eyes than ordinary optical imaging systems. This paper provides a comprehensive survey of the state-of-the-art work on artificial compound eyes. This review starts from natural compound eyes to artificial compound eyes including their system design, theoretical development and applications. The survey of artificial compound eyes is developed in terms of two main types: planar and curved artificial compound eyes. Finally, the most promising future research developments are highlighted.     

Production planning and control for remanufacturing: a state-of-the-art survey

Remanufacturing is rapidly emerging as an important form of waste prevention and environmentally conscious manufacturing. Firms are discovering it to be a profitable approach while at the same time enhancing their image as environmentally responsible, for a wide range of products. In this paper the characteristics of the remanufacturing environment are discussed first to distinguish this environment from other manufacturing environments. The production planning and control function of the remanufacturing firm is examined in this environment. The research in the various decision-making areas that comprise the production planning and control function is evaluated. There are many areas where the research is still scant. The lack of any overall integrated framework and models for the production planning and control function is noted. It is also pointed out that most firms are still grappling with these problems and do not have any formal mechanisms in place. There is a need to develop models and frameworks grounded in the problems and needs of these remanufacturing firms.     

Evolutionary computing in recommender systems: a review of recent research

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.     

Machine learning in continuous casting of steel: a state-of-the-art survey

Journal of Intelligent Manufacturing - Continuous casting is the most important route for the production of steel today. Due to the physical, mechanical, and chemical components involved in the...     

Machine learning for intrusion detection in MANET: a state-of-the-art survey

Machine learning consists of algorithms that are first trained with reference input to “learn” its specifics and then used on unseen input for classification purposes. Mobile ad-hoc wireless networks (MANETs) have drawn much attention to research community due to their advantages and growing demand. However, they appear to be more susceptible to various attacks harming their performance than any other kind of network. Intrusion Detection Systems represent the second line of defense against malevolent behavior to MANETs, since they monitor network activities in order to detect any malicious attempt performed by intruders. Due to the inherent distributed architecture of MANET, traditional cryptography schemes cannot completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying machine learning methods for IDS these challenges can be overcome. In this paper, we present the most prominent models for building intrusion detection systems by incorporating machine learning in the MANET scenario. We have structured our survey into four directions of machine learning methods: classification approaches, association rule mining techniques, neural networks and instance based learning approaches. We analyze the most well-known approaches and present notable achievements but also drawbacks or flaws that these methods have. Finally, in concluding our survey we provide some findings of paramount importance identifying open issues in the MANET field of interest.     

Collaborative recommender systems: Combining effectiveness and efficiency

Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the “big picture”, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms.     

Intelligent system for COVID-19 prognosis: a state-of-the-art survey

Applied Intelligence - This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus...     

Personalisation and recommender systems in digital libraries

Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field.     

A survey of the state-of-the-art in patch-based synthesis

Computational Visual Media - This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar...     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.