Perbandingan Interpretasi Analisis Data Page View dan Click Related Post Pada Website Edukasi Sebagai Implisit Feedback Dengan Hasil Interpretasi Penggunaan Teknologi EyeTracking

2021 ◽  
Vol 3 (2) ◽  
pp. 66-72
Author(s):  
Riad Taufik Lazwardi ◽  
Khoirul Umam
Keyword(s):  
Decision Process ◽  
Recommendation System ◽  
User Behavior ◽  
Implicit Feedback ◽  
Decision Making Process ◽  
Learning Material ◽  
Relative Preferences ◽  
Insight Into ◽  
Explicit Feedback ◽  
Data Page

The analysis used in this study uses the help of Google Analytics to understand how the user's behavior on the Calculus learning material educational website page. Are users interested in recommendation articles? The answer to this question provides insight into the user's decision process and suggests how far a click is the result of an informed decision. Based on these results, it is hoped that a strategy to generate feedback from clicks should emerge. To evaluate the extent to which feedback shows relevance, versus implicit feedback to explicit feedback collected manually. The study presented in this study differs in at least two ways from previous work assessing the reliability of implicit feedback. First, this study aims to provide detailed insight into the user decision-making process through the use of a recommendation system with an implicit feedback feature. Second, evaluate the relative preferences that come from user behavior (user behavior). This differs from previous studies which primarily assessed absolute feedback. 

scholarly journals Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Huazhen Liu ◽  
Wei Wang ◽  
Yihan Zhang ◽  
Renqian Gu ◽  
Yaqi Hao
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Heterogeneous Data ◽  
User Preference ◽  
Personalized Recommendation ◽  
Implicit Feedback ◽  
Network Training ◽  
Feedback Data ◽  
Explicit Feedback

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.

scholarly journals Sistema de recomendación de contenidos para libros electrónicos inteligentes [Contents recommender system for intelligent eBooks]

2012 ◽  
Author(s):  
Edward Rolando Núnez Valdez ◽  
Vicente García Díaz ◽  
Jordan Pascual Espada ◽  
Carlos Enrique Montenegro Marín ◽  
Juan Manuel Cueva Lovelle ◽  
...  
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Implicit Feedback ◽  
Electronic Books ◽  
Collective Knowledge ◽  
Content Recommendation ◽  
Explicit Feedback

Resumen Un sistema de recomendación de contenidos para libros electrónicos inteligentes permite construir conocimientos colectivos para un conjunto de usuarios de una red social. Basándose en el análisis del comportamiento, preferencias y antecedentes de lectura, ayuda a los usuarios a descubrir contenidos interesantes relacionados a su perfil. En este trabajo, se propone un modelo para una plataforma de recomendación de contenidos basado en la retroalimentación implícita que ayude a los usuarios a descubrir contenidos de su interés de forma automática y dinámica. Palabras ClaveACRIE. GIUG, libros electrónicos, retroalimentación implícita, retroalimentación explicita, Sistemas de recomendación.   Abstract A content recommendation system for intelligent electronic books can build collective knowledge to a set of social network users. Based on the analysis of the behavior, preferences and background reading, helps users discover interesting content related to their profile. In this paper, we propose a model for a content recommendation platform based on implicit feedback to help users to discover content on their interest, automatically and dynamically. Keywords ACRIE, eBooks, GIUG, implicit feedback, explicit feedback, recommendation systems. 

Understanding Health Behavior: An Integrated Model for Social Marketers

2008 ◽  
Vol 14 (2) ◽  
pp. 2-32 ◽  
Author(s):  
Magdalena Cismaru ◽  
Anne M. Lavack ◽  
Heather Hadjistavropoulos ◽  
Kim D. Dorsch
Keyword(s):  
Health Behavior ◽  
Social Marketing ◽  
Transtheoretical Model ◽  
Process Model ◽  
Decision Making Process ◽  
Extended Parallel Process Model ◽  
Behavioral Theories ◽  
Health Related ◽  
Social Marketing Campaigns ◽  
Insight Into

Many effective social marketing campaigns seek to change health-related behavior by utilizing various health-protective behavioral theories. In this article, we review and integrate three such theories: protection motivation theory (PMT), the extended parallel process model (EPPM), and the transtheoretical model (TTM). We highlight how EPPM and TTM can be used to refine PMT by adding insight into the decision-making process involved when consumers consider whether or not to follow a particular recommended health behavior. Specifically, the development of an integrated PMT model can provide insight into the characteristics of people more or less likely to change, what happens when persuasion fails, and what can be done to increase persuasion. Developing an integrated PMT model opens new avenues of research that have the potential to increase our understanding of behavior and assist in creating more persuasive social marketing campaigns.

Sorting the File Drawer: A Typology for Describing Unpublished Studies

2021 ◽  
pp. 174569162097983
Author(s):  
David A. Lishner
Keyword(s):  
Decision Process ◽  
Motivated Reasoning ◽  
Psychology Research ◽  
Research Dissemination ◽  
Unpublished Studies ◽  
Study Results ◽  
Publication Decision ◽  
Research Domains ◽  
File Drawer ◽  
Insight Into

A typology of unpublished studies is presented to describe various types of unpublished studies and the reasons for their nonpublication. Reasons for nonpublication are classified by whether they stem from an awareness of the study results (result-dependent reasons) or not (result-independent reasons) and whether the reasons affect the publication decisions of individual researchers or reviewers/editors. I argue that result-independent reasons for nonpublication are less likely to introduce motivated reasoning into the publication decision process than are result-dependent reasons. I also argue that some reasons for nonpublication would produce beneficial as opposed to problematic publication bias. The typology of unpublished studies provides a descriptive scheme that can facilitate understanding of the population of study results across the field of psychology, within subdisciplines of psychology, or within specific psychology research domains. The typology also offers insight into different publication biases and research-dissemination practices and can guide individual researchers in organizing their own file drawers of unpublished studies.

Agricultural Multifunctionality and Care Farming: Insight from the UK

2015 ◽  
Vol 4 (1) ◽  
pp. 74-86
Author(s):  
Paul Custance ◽  
Keith Walley ◽  
Gaynor Tate ◽  
Goksel Armagan
Keyword(s):  
Learning Material ◽  
Multifunctional Agriculture ◽  
Economic Support ◽  
International Audience ◽  
The Uk ◽  
Generic Points ◽  
Care Farming ◽  
Compare And Contrast ◽  
Insight Into

The purpose of the article is to provide insight into care farming and the role that it may play in agriltural multifunctionality. The paper outlines three case studies of care farming in the UK to compare and contrast the roles that such organizations may play in multifunctional agriculture. Although the work has the obvious limitation of being based on case-study care farms that are based in the UK, the findings are sufficiently generic to serve as valuable learning material for those interested in the subject and located anywhere in the world. The main finding from this study is that care farming can take many different forms but still contribute to agricultural multifunctionality. The study also confirms the important roles that economic support and favourable legislation play in successful care farming. The paper concludes that care farming is a legitimate form of agricultural multifunctionality but reminds those interested in setting up or promoting care farms of the need for a supportive economic and legislative environment. The paper provides contemporary insight into the concept of care farming as a form of agricultural multifunctionality. A number of generic points are made that should be of value to an international audience of academics researching in this area as well as students studying care farming and agricultural multifunctionality, farmers considering diversifying into care farming and politicians working to create a political and economic environment that may support care farms.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

scholarly journals PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Jiang ◽  
Ruijin Wang ◽  
Zhiyuan Xu ◽  
Yaodong Huang ◽  
Shuo Chang ◽  
...  
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
User Behavior ◽  
Network Services ◽  
Mobile Social Network ◽  
Fine Grained ◽  
Friend Recommendation ◽  
Social Applications ◽  
Browsing Behavior ◽  
Behavioral Information

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

Publishing quantitative careers research: challenges and recommendations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bert Schreurs ◽  
Angus Duff ◽  
Pascale M. Le Blanc ◽  
Thomas H. Stone
Keyword(s):  
Design Methodology ◽  
Team Member ◽  
Research Question ◽  
Editorial Team ◽  
Board Members ◽  
Decision Making Process ◽  
Cross Sectional ◽  
Content Type ◽  
Insight Into

Purpose This article aims to provide prospective authors guidelines that will hopefully enable them to submit more competitive manuscripts to journals publishing careers research.Design/methodology/approach Based on their experience as an author, reviewer and editorial team member, the authors identify the main criteria that a quantitative study must meet to be considered for publication in international peer-reviewed journals covering career-related topics. They emphasize the importance of contributing to the careers literature and of designing the study in accordance with the research question.Findings Manuscripts are rejected because they are insufficiently innovative, and/or because sample, instruments and design are not appropriate to answer the research question at hand. Cross-sectional designs cannot be used to answer questions of mediation but should not be discarded automatically since they can be used to address other types of questions, including questions about nesting, clustering of individuals into subgroups, and to some extent, even causality.Originality/value The manuscript provides an insight into the decision-making process of reviewers and editorial board members and includes recommendations on the use of cross-sectional data.

An Evacuation Decision Making Model for Firefighters in Ad Hoc Robot Network

2013 ◽  
Vol 756-759 ◽  
pp. 504-508
Author(s):  
De Min Li ◽  
Jian Zou ◽  
Kai Kai Yue ◽  
Hong Yun Guan ◽  
Jia Cun Wang
Keyword(s):  
Decision Making ◽  
Markov Decision Process ◽  
Decision Process ◽  
Ad Hoc ◽  
Decision Making Process ◽  
Decision Method ◽  
Markov Decision ◽  
Critical Problems ◽  
Fire Scene ◽  
Decision Making Model

Evacuation for a firefighter in complex fire scene is challenge problem. In this paper, we discuss a firefighters evacuation decision making model in ad hoc robot network on fire scene. Due to the dynamics on fire scene, we know that the sensed information in ad hoc robot network is also dynamically variance. So in this paper, we adapt dynamic decision method, Markov decision process, to model the firefighters decision making process for evacuation from fire scene. In firefighting decision making process, we know that the critical problems are how to define action space and evaluate the transition law in Markov decision process. In this paper, we discuss those problems according to the triangular sensors situation in ad hoc robot network and describe a decision making model for a firefighters evacuation the in the end.

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