scholarly journals Recommender Systems: Past, Present, Future

AI Magazine ◽  
2022 ◽  
Vol 42 (3) ◽  
pp. 3-6
Author(s):  
Dietmar Jannach ◽  
Pearl Pu ◽  
Francesco Ricci ◽  
Markus Zanker
Keyword(s):  
Human Computer Interaction ◽  
Recommender Systems ◽  
Information Search ◽  
Information Filtering ◽  
General Purpose ◽  
Learning Technology ◽  
Computerized System ◽  
Interaction Problem ◽  
Selection Of ◽  
Computer Interaction

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the fore-front. It features articles on the latest learning technology, reflects on the human-computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.

A review of a recommendation filtering system approach based on reliable sustainable opinion mining

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imen Gmach ◽  
Nadia Abaoub ◽  
Rubina Khan ◽  
Naoufel Mahfoudh ◽  
Amira Kaddour
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Search ◽  
Recommendation System ◽  
Opinion Mining ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Content Filtering ◽  
Content Type ◽  
The Impact

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems

1989 ◽  
Vol 4 (3) ◽  
pp. 205-233 ◽  
Author(s):  
E. T. Keravnou ◽  
J. Washbrook
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Human Computer Interaction ◽  
Expert Systems ◽  
First Generation ◽  
Second Generation ◽  
Robust Solutions ◽  
Definition Of ◽  
Interaction Problem ◽  
Computer Interaction

AbstractFirst-generation expert systems have significant limitations, often attributed to their not being sufficiently deep. However, a generally accepted answer to “What is a deep expert system?” is still to be given. To answer this question one needs to answer “Why do first-generation systems exhibit the limitations they do?” thus identifying what is missing from first-generation systems and therefore setting the design objectives for second-generation (i.e. deep) systems. Several second-generation architectures have been proposed; inherent in each of these architectures is a definition of deepness. Some of the proposed architectures have been designed with the objective of alleviating a subset, rather than the whole set, of the first-generation limitations. Such approaches are prone to local, non-robust solutions. In this paper we analyze the limitations (under the categories: human-computer interaction, problem-solving flexibility, and extensibility) of the first-generation expert systems thus setting design goals for second-generation systems. On the basis of this analysis proposed second-generation architectures are reviewed and compared. The paper concludes by presenting requirements for a generic second-generation architecture.

scholarly journals GUI design exploration software for microwave antennas

2017 ◽  
Vol 4 (4) ◽  
pp. 274-281 ◽  
Author(s):  
Bo Liu ◽  
Alexander Irvine ◽  
Mobayode O. Akinsolu ◽  
Omer Arabi ◽  
Vic Grout ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
State Of The Art ◽  
General Purpose ◽  
Simulation Software ◽  
Antenna Design ◽  
Design Exploration ◽  
Design Engineers ◽  
Exploration Tool ◽  
Optimization Efficiency ◽  
Computer Interaction

Abstract Optimizers in commercial electromagnetic (EM) simulation software packages are the main tools for performing antenna design exploration today. However, these general purpose optimizers are facing challenges in optimization efficiency, supported optimization types and usability for antenna experts without deep knowledge on optimization. Aiming to fill the gaps, a new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. The key features are: (1) State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but unable to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools); (2) Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers, such as automatic algorithmic parameter setting and interactive stopping criteria; (3) Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages. A case study verifies the advantages of ADE. Highlights A new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but difficult to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools). Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers. Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages.

scholarly journals A low cost SSVEP-EEG based human-computer-interaction system for completely locked-in patients

2021 ◽  
Vol 10 (4) ◽  
pp. 2245-2253
Author(s):  
Azhar Dilshad ◽  
Vali Uddin ◽  
Muhammad Rizwan Tanweer ◽  
Tariq Javid
Keyword(s):  
Human Computer Interaction ◽  
Human Life ◽  
Low Cost ◽  
General Purpose ◽  
Control Panel ◽  
Motor Disability ◽  
Brain Signals ◽  
Conventional Manner ◽  
Application Specific ◽  
Computer Interaction

Human computer interaction (HCI) for completely locked-in patients is a very difficult task. Nowadays, information technology (IT) is becoming an essential part of human life. Patients with completely locked-in state are generally unable to facilitate themselves by these useful technological advancements. Hence, they cannot use modern IT gadgets and applications throughout the lifespan after disability. Advancements in brain computer interface (BCI) enable operating IT devices using brain signals specifically when a person is unable to interact with the devices in conventional manner due to cognitive motor disability. However, existing state-of-the-art application specific BCI devices are comparatively too expensive. This paper presents a research and development work that aims to design and develop a low-cost general purpose HCI system that can be used to operate computers and a general purpose control panel through brain signals. The system is based on steady state visual evoked potentials (SSVEP). In proposed system, these electrical signals are obtained in response of a number of different flickering lights of different frequencies through electroencephalogram (EEG) electrodes and an open source BCI hardware. Successful trails conducted on healthy participants suggest that severely paralyzed subjects can operate a computer or control panel as an alternative to conventional HCI device.

scholarly journals Team PhyPA: Brain-Computer Interfacing for Everyday Human-Computer Interaction

2017 ◽  
Vol 61 (2) ◽  
pp. 209 ◽  
Author(s):  
Thorsten O. Zander ◽  
Laurens R. Krol
Keyword(s):  
Human Computer Interaction ◽  
Brain Activity ◽  
Brain Computer Interfaces ◽  
Input Channel ◽  
Computer Interfaces ◽  
Brain Computer Interfacing ◽  
Work Done ◽  
Selection Of ◽  
Computer Interfacing ◽  
Computer Interaction

Brain-computer interfaces can provide an input channel from humans to computers that depends only on brain activity, bypassing traditional means of communication and interaction. This input channel can be used to send explicit commands, but also to provide implicit input to the computer. As such, the computer can obtain information about its user that not only bypasses, but also goes beyond what can be communicated using traditional means. In this form, implicit input can potentially provide significant improvements to human-computer interaction. This paper describes a selection of work done by Team PhyPA (Physiological Parameters for Adaptation) at the Technische Universität Berlin to use brain-computer interfacing to enrich human-computer interaction.

scholarly journals Informing through User-Centered Exploratory Search and Human-Computer Interaction Strategies

10.28945/3282 ◽  
2008 ◽  
Author(s):  
Panagiotis Petratos
Keyword(s):  
Information Retrieval ◽  
Human Computer Interaction ◽  
Human Factors ◽  
General Information ◽  
Exploratory Search ◽  
Human Participants ◽  
Search Quality ◽  
Selection Of ◽  
Computer Interaction ◽  
User Centered

In this article the subject of Informing through user-centered Exploratory Search and Information Retrieval utilizing human-computer interaction strategies is analyzed. Exploratory Search is a new field that has sprung from the more general Information Retrieval. Informing Science is a trans-discipline which transcends a large variety of fields and seeks how to best inform all the clients of interest. One facet of Informing Science, the process of elucidating the best methods of informing inquiring clientele, is served by user-centered Exploratory Search and human-computer interaction strategies. This work explains a human factors method which allows the comparison of the performance of multiple IR systems and can enhance the comparative topic focused IR search quality. This human factors method also allows the human participants to provide their IR explicit feedback and record these judgments as a gold standard for future comparison. This human factors method is tested by established statistical analysis and allows the statistical comparison of the IR performance of a selection of IR systems. This work also demonstrates the results of this human factors method after testing it upon three leading IR systems, Google, Yahoo and Live Search.

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