The Next Generation of Personalization Techniques

2011 ◽  
pp. 72-92
Author(s):  
Gulden Uchyigit
Keyword(s):  
Knowledge Representation ◽  
Traditional Method ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
User Preferences ◽  
Future Trends ◽  
User Profiles ◽  
User Interests ◽  
Key Concepts

Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization approaches model user preferences and store them in user profiles, used to deliver personalized content. A traditional method for profile representation is the so called keyword-based representation, where the user interests are modelled using keywords which are selected from the contents of the items which the user has rated. Although, keyword based approaches are simple and are extensively used for profile representation they fail to represent semantic-based information, this information is lost during the pre-processing phase. Future trends in personalization systems necessitate more innovative personalization techniques that are able to capture rich semanticbased information during the representation, modelling and learning phases. In recent years ontologies (key concepts and along with their interrelationships) to express semantic-based information have been very popular in domain knowledge representation. The primary goal of this chapter is to present an overview of the state-of-the art techniques and methodologies which aim to integrate personalization technologies with semantic-based information.

State of the Art Recommendation Approaches

2020 ◽  
pp. 1621-1651
Author(s):  
Bhupesh Rawat ◽  
Sanjay K. Dwivedi
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
New Techniques ◽  
Contextual Data ◽  
Additional Information ◽  
E Learning ◽  
Traditional Approaches

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.

scholarly journals OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 125
Author(s):  
Maria A. Cornejo-Lupa ◽  
Yudith Cardinale ◽  
Regina Ticona-Herrera ◽  
Dennis Barrios-Aranibar ◽  
Manoel Andrade ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Autonomous Robots ◽  
State Of The Art ◽  
Formal Representation ◽  
Robot Operating System ◽  
Localization And Mapping ◽  
Complex Knowledge ◽  
Structural Levels ◽  
Mapping Information

Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics.

A Navigational Aid for Blind Pedestrians Designed with User- and Activity-Centered Approaches

2010 ◽  
pp. 389-407
Author(s):  
Florence Gaunet ◽  
Xavier Briffault
Keyword(s):  
Case Studies ◽  
Design Process ◽  
Urban Areas ◽  
Visually Impaired ◽  
State Of The Art ◽  
Future Trends ◽  
User Profiles ◽  
Production Rules ◽  
Navigational Aid ◽  
Route Descriptions

The two-fold aim of this chapter is to present the design process of an interface for a mobile navigational aid for blind pedestrians and a set of rules for producing route descriptions for these users, as well as the methodology used to develop them, rooted in a user- and activity-centered approach. We first present the state of the art of wearable verbal navigational aids and what might still be lacking in their conception, and propose a reusable user- and activity-centered approach designed to complement already existing and future systems. Case studies fitting into this approach are next presented: route descriptions produced by blind pedestrians were analyzed; the production rules were extracted and tested in urban areas. Results reveal these rules, the specific database features, the required user profiles, and the precision of localization necessary for assisting blind pedestrians’ wayfinding in urban areas. Finally, future trends in mobile guiding tools for the visually impaired are examined.

A Navigational Aid for Blind Pedestrians Designed with User- and Activity-Centered Approaches

2008 ◽  
pp. 693-710 ◽  
Author(s):  
Florence Gaunet ◽  
Xavier Briffault
Keyword(s):  
Case Studies ◽  
Design Process ◽  
Urban Areas ◽  
Visually Impaired ◽  
State Of The Art ◽  
Future Trends ◽  
User Profiles ◽  
Production Rules ◽  
Navigational Aid ◽  
Route Descriptions

The two-fold aim of this chapter is to present the design process of an interface for a mobile navigational aid for blind pedestrians and a set of rules for producing route descriptions for these users, as well as the methodology used to develop them, rooted in a user- and activity-centered approach. We first present the state of the art of wearable verbal navigational aids and what might still be lacking in their conception, and propose a reusable user- and activity-centered approach designed to complement already existing and future systems. Case studies fitting into this approach are next presented: route descriptions produced by blind pedestrians were analyzed; the production rules were extracted and tested in urban areas. Results reveal these rules, the specific database features, the required user profiles, and the precision of localization necessary for assisting blind pedestrians’ wayfinding in urban areas. Finally, future trends in mobile guiding tools for the visually impaired are examined.

State of the Art Recommendation Approaches

2018 ◽  
Vol 10 (1) ◽  
pp. 51-76
Author(s):  
Bhupesh Rawat ◽  
Sanjay K. Dwivedi
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
New Techniques ◽  
Contextual Data ◽  
Additional Information ◽  
E Learning ◽  
Traditional Approaches

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.

scholarly journals Application of Microextraction-Based Techniques for Screening-Controlled Drugs in Forensic Context—A Review

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2168
Author(s):  
Samir M. Ahmad ◽  
Oriana C. Gonçalves ◽  
Mariana N. Oliveira ◽  
Nuno R. Neng ◽  
José M. F. Nogueira
Keyword(s):  
Environmental Sustainability ◽  
Illicit Drugs ◽  
State Of The Art ◽  
Cost Benefit ◽  
Future Trends ◽  
Present Contribution ◽  
Controlled Drugs ◽  
Related Compounds ◽  
Innovative Techniques ◽  
Great Simplicity

The analysis of controlled drugs in forensic matrices, i.e., urine, blood, plasma, saliva, and hair, is one of the current hot topics in the clinical and toxicological context. The use of microextraction-based approaches has gained considerable notoriety, mainly due to the great simplicity, cost-benefit, and environmental sustainability. For this reason, the application of these innovative techniques has become more relevant than ever in programs for monitoring priority substances such as the main illicit drugs, e.g., opioids, stimulants, cannabinoids, hallucinogens, dissociative drugs, and related compounds. The present contribution aims to make a comprehensive review on the state-of-the art advantages and future trends on the application of microextraction-based techniques for screening-controlled drugs in the forensic context.

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

scholarly journals Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Neha M. Jain ◽  
Alison Culley ◽  
Teresa Knoop ◽  
Christine Micheel ◽  
Travis Osterman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Knowledge Representation ◽  
Conceptual Framework ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Prospective Clinical Trial ◽  
Future Trends ◽  
Current State ◽  
Evaluation Strategies

In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement.

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