scholarly journals Female-Type Android’s Drive to Quickly Understand a User’s Concept of Preferences Stimulates Dialogue Satisfaction: Dialogue Strategies for Modeling User’s Concept of Preferences

2021 ◽  
Author(s):  
Takahisa Uchida ◽  
Takashi Minato ◽  
Yutaka Nakamura ◽  
Yuichiro Yoshikawa ◽  
Hiroshi Ishiguro
Keyword(s):  
User Satisfaction ◽  
Design Theory ◽  
User Preferences ◽  
Willingness To Communicate ◽  
User Preference ◽  
Female Type ◽  
Task Oriented ◽  
Android Robot ◽  
User Motivation

AbstractThis research develops a conversational robot that stimulates users’ dialogue satisfaction and motivation in non-task-oriented dialogues that include opinion and/or preference exchanges. One way to improve user satisfaction and motivation is by demonstrating the robot’s ability to understand user opinions. In this paper, we explore a method that efficiently obtains the concept of user preferences: likes and dislikes. The concept is acquired by complementing a small amount of user preference data observed in dialogues. As a method for efficient collection, we propose a dialogue strategy that creates utterances with the largest expected complementation. Our experimental results with a female-type android robot suggest that the proposed strategy efficiently obtained user preferences and enhanced dialogue satisfaction. In addition, the strength of user motivation (i.e., long-term willingness to communicate with the android) is only positively correlated with the android’s willingness to understand. Our results not only show the effectiveness of our proposed strategy but also suggest a design theory for dialogue robots to stimulate dialogue motivation, although the current results are derived only from a female-type android.

scholarly journals AIBOStory – Autonomous Robots supporting Interactive, Collaborative Story-telling

2013 ◽  
Vol 4 (1) ◽  
Author(s):  
Fotios Papadopoulos ◽  
Kerstin Dautenhahn ◽  
Wan Ching Ho
Keyword(s):  
Autonomous Robots ◽  
Autonomous Robot ◽  
User Preferences ◽  
Human Robot Interaction ◽  
User Preference ◽  
Story Telling ◽  
Remote Communication ◽  
Robot Interaction ◽  
Communication Software

AbstractThis article describes the design and evaluation of AIBOStory - a novel, remote interactive story telling system that allows users to create and share common stories through an integrated, autonomous robot companion acting as a social mediator between two remotely located people. The behaviour of the robot was inspired by dog behaviour, including a simple computational memory model. AIBOStory has been designed to work alongside online video communication software and aims to enrich remote communication experiences over the internet. An initial pilot study evaluated the proposed system’s use and acceptance by the users. Five pairs of participants were exposed to the system, with the robot acting as a social mediator, and the results suggested an overall positive acceptance response. The main study involved long-term interactions of 20 participants using AIBOStory in order to study their preferences between two modes: using the game enhanced with an autonomous robot and a non-robot mode which did not use the robot. Instruments used in this study include multiple questionnaires from different communication sessions, demographic forms and logged data from the robots and the system. The data was analysed using quantitative and qualitative techniques to measure user preference and human-robot interaction. The statistical analysis suggests user preferences towards the robot mode.

Personalization Approaches for Ranking

2017 ◽  
Vol 7 (1) ◽  
pp. 1-16
Author(s):  
Madhuri A. Potey ◽  
Pradeep K. Sinha
Keyword(s):  
User Satisfaction ◽  
State Of The Art ◽  
User Model ◽  
Research Trends ◽  
User Preferences ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Information Need ◽  
And Behavior ◽  
Available Information

Search engine technologies are evolving to satisfy the user's ever increasing information need; but are yet to achieve perfection especially in ranking. With the exponential growth in the available information on the internet; ranking has become vital for satisfactory search experience. User satisfaction can be ensured to some extent by personalizing the search results based on user preferences which can be explicitly stated or learned from user's search behavior. Machine learning algorithms which predict user preference from the available information related to the user are extensively experimented for personalization. Among several studies undertaken for re-ranking the documents, many focus on the user. Such approaches create user model to capture the search context and behavior. This study attempts to analyze the research trends in user model based personalization and discuss experimental results in personalized information retrieval area. The authors experimented to extend the state of the art in the specific areas of personalization.

Design and Manufacturing of 3D Printed Foods With User Validation

2020 ◽  
Author(s):  
Stefania Chirico Scheele ◽  
Martin Binks ◽  
Paul F. Egan
Keyword(s):  
Additive Manufacturing ◽  
User Satisfaction ◽  
Three Dimensional ◽  
User Preferences ◽  
User Preference ◽  
Great Promise ◽  
High Fidelity ◽  
Standard Design ◽  
3D Printed ◽  
Overhang Angle

Abstract Additive manufacturing is becoming widely practical for diverse engineering applications, with emerging approaches showing great promise in the food industry. From the realization of complex food designs to the automated preparation of personalized meals, 3D printing promises many innovations in the food manufacturing sector. However, its use is limited due to the need to better understand manufacturing capabilities for different food materials and user preferences for 3D food prints. Our study aims to explore the 3D food printability of design features, such as overhangs and holes, and assess how well they print through quantitative and qualitative measurements. Designs with varied angles and diameters based on the standard design limitations for additive manufacturing were printed and measured using marzipan and chocolate. It was found that marzipan material has a minimum feature size for overhang design at 55° and for hole design at 4mm, while chocolate material has a minimum overhang angle size of 35° and does not reliably print holes. Users were presented a series of designs to determine user preference (N = 30) towards the importance of fidelity and accuracy between the expected design and the 3D printed sample, and how much they liked each sample. Results suggest that users prefer designs with high fidelity to their original shape and perceive the current accuracy/precision of 3D printers sufficient for accurately printing three-dimensional geometries. These results demonstrate the current manufacturing capabilities for 3D food printing and success in achieving high fidelity designs for user satisfaction. Both of these considerations are essential steps in providing automated and personalized manufacturing for specific user needs and preferences.

scholarly journals Modeling user preference dynamics with coupled tensor factorization for social media recommendation

2020 ◽  
Author(s):  
Hamidreza Tahmasbi ◽  
Mehrdad Jalali ◽  
Hassan Shakeri
Keyword(s):  
Social Media ◽  
User Satisfaction ◽  
User Preferences ◽  
User Preference ◽  
Tensor Factorization ◽  
Plausible Assumption ◽  
The Past ◽  
Proposed Model ◽  
Recommendation Accuracy ◽  
Over Time

AbstractAn essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.

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 Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

2021 ◽  
Vol 11 (3) ◽  
pp. 1064
Author(s):  
Jenq-Haur Wang ◽  
Yen-Tsang Wu ◽  
Long Wang
Keyword(s):  
Social Media ◽  
Feature Fusion ◽  
Relevant Information ◽  
Image Features ◽  
User Preferences ◽  
User Preference ◽  
Late Fusion ◽  
Multimodal Features ◽  
Fusion Methods ◽  
Text Features

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

2021 ◽  
pp. 1063293X2110195
Author(s):  
Ying Yu ◽  
Shan Li ◽  
Jing Ma
Keyword(s):  
Time Series Prediction ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Selection Methods ◽  
Qos Prediction ◽  
Qos Performance ◽  
Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

Crossing cultural borders: A case study of conceptual design outcomes of U.S. and Moroccan student samples

2021 ◽  
pp. 1-42
Author(s):  
Aoran Peng ◽  
Jessica Menold ◽  
Scarlett Miller
Keyword(s):  
Global Economy ◽  
Design Theory ◽  
Design Thinking ◽  
Emerging Market ◽  
Gender Effects ◽  
Concept Generation ◽  
Design Theory And Methodology ◽  
Key Aspects ◽  
The U.S

Abstract There has been a plethora of design theory and methodology research conducted to answer important questions centered around how ideas are developed and translated into successful products. Understanding this is vital because of the role creativity and innovation have in long-term economic success. However, most of this research have focused on U.S. samples, leaving to question if differences exist across cultural borders. Answering this question is key to supporting a successful global economy. The current work provides a first step at answering this question by examining similarities and differences in concept generation and screening practices between students in an emerging market, Morocco, and those in a more established market, the U.S during a design thinking workshop. Our results show that while students in the U.S. sample produced more ideas than the Moroccan sample, there was no difference in the perceived quality of ideas generated (idea goodness). In addition, while U.S. women were found to produce more ideas than U.S. men, there were no gender effects for students in the Moroccan sample. Finally, the results show that ideas with low goodness had a higher probability of passing concept screening if it was evaluated by its owner regardless of the population studied – identifying the potential impact of ownership bias across cultures. As a whole, these results suggest that key aspects of design theory and methodology research may in fact translate across cultures but also identified key areas for further investigation.

scholarly journals Evolutionary Social Poisson Factorizationfor Temporal Recommendation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
ChunYan Yin ◽  
YongHeng Chen ◽  
Wanli Zuo
Keyword(s):  
Recommendation Systems ◽  
Predictive Performance ◽  
User Preferences ◽  
Social Model ◽  
User Preference ◽  
Latent Factors ◽  
Factorization Model ◽  
Real World Datasets ◽  
Item Attributes ◽  
The Impact

AbstractPreference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF$$\_$$ _ Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF$$\_$$ _ Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.

scholarly journals Women Leadership in Indian Organizations

2020 ◽  
pp. 23-30
Author(s):  
Dr. Meenakshi Kaushik
Keyword(s):  
Global Economy ◽  
Women Leadership ◽  
Global Trends ◽  
Managerial Practices ◽  
Women In Business ◽  
Organizational Orientation ◽  
Current Financial Crisis ◽  
Task Oriented ◽  
Conceptual Research

In today’s global economy, industries require a talented pool of candidates to create a comparative competitive advantage to address the global opportunities as well as to address these global trends and dynamic challenges factual and cognitive leadership is required to manage changes effectively. The nurturing and task-oriented style, managerial practices, organizational orientation, especially followed by women entrepreneurs/ leaders/ managers has carved a niche for women leadership in national as well as international platforms. The current financial crisis and long-term global trends are reshaping the corporate landscape, and there is an urgent need to accelerate some of the changes in corporations to seize the new opportunities that arise from time to time. This conceptual research paper analyzes that though the ages, how women have experienced the disadvantages of existing in a patriarchal framework designating them in a homemaker role and how women in business now, have broken that mold across the world and created new stories for themselves.

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