scholarly journals Humanized Recommender Systems: State-of-the-art and Research Issues

2021 ◽  
Vol 11 (2) ◽  
pp. 1-41
Author(s):  
Thi Ngoc Trang Tran ◽  
Alexander Felfernig ◽  
Nava Tintarev
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Psychological Factors ◽  
System Development ◽  
Social Connections ◽  
Group Recommendation ◽  
Research Issues ◽  
Decision Biases ◽  
Single User ◽  
User Recommendation

Psychological factors such as personality, emotions, social connections , and decision biases can significantly affect the outcome of a decision process. These factors are also prevalent in the existing literature related to the inclusion of psychological aspects in recommender system development. Personality and emotions of users have strong connections with their interests and decision-making behavior. Hence, integrating these factors into recommender systems can help to better predict users’ item preferences and increase the satisfaction with recommended items. In scenarios where decisions are made by groups (e.g., selecting a tourism destination to visit with friends), group composition and social connections among group members can affect the outcome of a group decision. Decision biases often occur in a recommendation process, since users usually apply heuristics when making a decision. These biases can result in low-quality decisions. In this article, we provide a rigorous review of existing research on the influence of the mentioned psychological factors on recommender systems. These factors are not only considered in single-user recommendation scenarios but, importantly, also in group recommendation ones, where groups of users are involved in a decision-making process. We include working examples to provide a deeper understanding of how to take into account these factors in recommendation processes. The provided examples go beyond single-user recommendation scenarios by also considering specific aspects of group recommendation settings.

scholarly journals A Taxonomy for Generating Explanations in Recommender Systems

AI Magazine ◽  
2011 ◽  
Vol 32 (3) ◽  
pp. 90-98 ◽  
Author(s):  
Gerhard Friedrich ◽  
Markus Zanker
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Research Issues ◽  
Open Research ◽  
Unified View ◽  
Similarities And Differences ◽  
Future Work

In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Finally, the authors present their view on open research issues and opportunities for future work on this topic.

scholarly journals A Qualitative Study on the US Forest Service’s Risk Management Assistance Efforts to Improve Wildfire Decision-Making

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 344
Author(s):  
Courtney A. Schultz ◽  
Lauren F. Miller ◽  
Sarah Michelle Greiner ◽  
Chad Kooistra
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Decision Support ◽  
Qualitative Study ◽  
Fire Management ◽  
Perceived Value ◽  
Forest Service ◽  
The Us ◽  
Decision Biases ◽  
Line Officers

To support improved wildfire incident decision-making, in 2017 the US Forest Service (Forest Service) implemented risk-informed tools and processes, together known as Risk Management Assistance (RMA). The Forest Service is developing tools such as RMA to improve wildfire decision-making and implements these tools in complex organizational environments. We assessed the perceived value of RMA and factors that affected its use to inform the literature on decision support for fire management. We sought to answer two questions: (1) What was the perceived value of RMA for line officers who received it?; and (2) What factors affected how RMA was received and used during wildland fire events? We conducted a qualitative study involving semi-structured interviews with decision-makers to understand the contextualized and interrelated factors that affect wildfire decision-making and the uptake of a decision-support intervention such as RMA. We used a thematic coding process to analyze our data according to our questions. RMA increased line officers’ ability to communicate the rationale underlying their decisions more clearly and transparently to their colleagues and partners. Our interviewees generally said that RMA data analytics were valuable but did not lead to changes in their decisions. Line officer personality, pre-season exposure to RMA, local political dynamics and conditions, and decision biases affected the use of RMA. Our findings reveal the complexities of embracing risk management, not only in the context of US federal fire management, but also in other similar emergency management contexts. Attention will need to be paid to existing decision biases, integration of risk management approaches in the interagency context, and the importance of knowledge brokers to connect across internal organizational groups. Our findings contribute to the literature on managing change in public organizations, specifically in emergency decision-making contexts such as fire management.

scholarly journals Explaining Review-Based Recommendations: Effects of Profile Transparency, Presentation Style and User Characteristics

i-com ◽  
2020 ◽  
Vol 19 (3) ◽  
pp. 181-200
Author(s):  
Diana C. Hernandez-Bocanegra ◽  
Jürgen Ziegler
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
User Interface ◽  
Recommender Systems ◽  
Social Awareness ◽  
User Characteristics ◽  
Presentation Style ◽  
User Reviews ◽  
Decision Making Styles

Abstract Providing explanations based on user reviews in recommender systems (RS) may increase users’ perception of transparency or effectiveness. However, little is known about how these explanations should be presented to users, or which types of user interface components should be included in explanations, in order to increase both their comprehensibility and acceptance. To investigate such matters, we conducted two experiments and evaluated the differences in users’ perception when providing information about their own profiles, in addition to a summarized view on the opinions of other customers about the recommended hotel. Additionally, we also aimed to test the effect of different display styles (bar chart and table) on the perception of review-based explanations for recommended hotels, as well as how useful users find different explanatory interface components. Our results suggest that the perception of an RS and its explanations given profile transparency and different presentation styles, may vary depending on individual differences on user characteristics, such as decision-making styles, social awareness, or visualization familiarity.

Interfaces and Human Decision Making for Recommender Systems

2020 ◽  
Author(s):  
Peter Brusilovsky ◽  
Marco de Gemmis ◽  
Alexander Felfernig ◽  
Pasquale Lops ◽  
John O'Donovan ◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Human Decision

The Effect of Visual Illusions on the Graphical Display of Information

2010 ◽  
Vol 24 (1) ◽  
pp. 23-42 ◽  
Author(s):  
T. S. Amer ◽  
Sury Ravindran
Keyword(s):  
Decision Making ◽  
Decision Aids ◽  
Three Dimensional ◽  
Accounting Information ◽  
Theoretical Work ◽  
Visual Illusions ◽  
Graphical Display ◽  
Two Dimensional ◽  
Graphical Displays ◽  
Decision Biases

ABSTRACT: Graphical displays of business and accounting information are widely used as decision aids. Theoretical work in visual perception indicates graphs that exhibit certain characteristics create visual illusions that may result in biased decision making. This paper reports the results of an experiment that demonstrates how such two-dimensional and three-dimensional visual illusions cause viewers to make biased comparison judgments. The experiment also shows that these decision biases can be mitigated by including gridlines in both two- and three-dimensional graphs.

scholarly journals Evacuation Decision-Making during Hurricane Matthew: An Assessment of the Effects of Social Connections

2017 ◽  
Vol 9 (4) ◽  
pp. 769-776 ◽  
Author(s):  
Jennifer Collins ◽  
Robin Ersing ◽  
Amy Polen
Keyword(s):  
Decision Making ◽  
Disaster Preparedness ◽  
Three Dimensions ◽  
Social Connections ◽  
Storm Event ◽  
Community Based ◽  
Memory Decay ◽  
Perceived Access ◽  
The Relationship

Abstract This study conducted in Florida examines the relationship between an individual’s social connections and their decision to evacuate during a hurricane warning. Using Hurricane Matthew in 2016 as a case study, a survey was conducted on two groups (those who evacuated and those who did not), assessing one’s social connections considering three dimensions: dependability, density, and diversity. These factors, in addition to socioeconomic variables (e.g., age, race, education), were used to better define a picture for what influences evacuation decision-making. To avoid memory decay, the surveys were completed at the time of the evacuation for those who evacuated and immediately after the passage of Matthew for those who did not evacuate. It was concluded, through statistical analyses, that the perceived dependability of a person’s social connections (i.e., their perceived access to resources and support) played a significant role in the decision to evacuate or not, with non-evacuees having more dependable relationships and having a tightknit community they can rely on during a storm event. On the other hand, the density and diversity of peoples’ social connections did not significantly impact the decision to evacuate. This study has important implications for adding to the knowledge base on community-based sustainable disaster preparedness and resilience.

scholarly journals PSYCHOLOGICAL FACTORS OF DECISION-MAKING ABOUT VACCINATION

2016 ◽  
pp. 125-136 ◽  
Author(s):  
VladimirK. Solondaev ◽  
◽  
Elena V. Koneva ◽  
Natalia L. Chernaia ◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Psychological Factors

scholarly journals Computational intelligence in product design engineering: review and trends

2021 ◽  
Author(s):  
Filippo A. Salustri
Keyword(s):  
Decision Making ◽  
Product Design ◽  
Engineering Design ◽  
Computational Intelligence ◽  
Design Automation ◽  
State Of The Art ◽  
Design Engineering ◽  
Broad Perspective ◽  
Research Issues ◽  
Product Engineering

Product design engineering is undergoing a transformation from informal and largely experience-based discipline to a science-based domain. Computational intelligence offers models and algorithms that can contribute greatly to design formalization and automation. This paper surveys computational intelligence concepts and approaches applicable to product design engineering. Taxonomy of the surveyed literature is presented according to the generally recognized areas in both product design engineering and computational intelligence. Some research issues that arise from the broad perspective presented in the paper have been signaled but not fully pursued. No survey of such a broad field can be complete, however, the material presented in the paper is a summary of state-of-the-art computational intelligence concepts and approaches in product design engineering. Keywords: Computational intelligence, engineering design, product engineering, decision making, design automation

Regret in Decision Making

2002 ◽  
Vol 11 (6) ◽  
pp. 212-216 ◽  
Author(s):  
Terry Connolly ◽  
Marcel Zeelenberg
Keyword(s):  
Decision Making ◽  
Comparative Evaluation ◽  
The Other ◽  
Research Issues ◽  
New Approach ◽  
Self Blame ◽  
Psychology And Economics ◽  
Core Components ◽  
Decision Research

Decision research has only recently started to take seriously the role of emotions in choices and decisions. Regret is the emotion that has received the most attention. In this article, we sample a number of the initial regret studies from psychology and economics, and trace some of the complexities and contradictions to which they led. We then sketch a new theory, decision justification theory (DJT), which synthesizes several apparently conflicting findings. DJT postulates two core components of decision–related regret, one associated with the (comparative) evaluation of the outcome, the other with the feeling of self–blame for having made a poor choice. We reinterpret several existing studies in DJT terms. We then report some new studies that directly tested (and support) DJT, and propose a number of research issues that follow from this new approach to regret.

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