An Exploratory Study of Ontology-Based Platform Analysis Under User Preference Uncertainty

2012 ◽  
Author(s):  
Soon Chong Johnson Lim ◽  
Ying Liu ◽  
Han Tong Loh
Keyword(s):  
Bayesian Network ◽  
New Product Development ◽  
Product Family ◽  
User Preferences ◽  
User Preference ◽  
Knowledge Modeling ◽  
Laptop Computer ◽  
Preference Uncertainty ◽  
Network Representation ◽  
Customer Preferences

Analysis of user preference is among the crucial tasks at early stages of new product development (NPD). In order to satisfy diversified user preferences in the market, product companies have struggled to design a variety of products to address different customer voices. In this context, product family design (PFD) is a widely adopted strategy to deal with such product realization needs. Besides preference diversity, uncertainty of user preference is another important aspect that can greatly affect product design and offerings especially when customer preferences are not clear, not fully identified, or have drifted overtime. Previously, we have studied an ontology-based information representation for PFD, which offers a modeling scheme to assist multi-faceted product variant derivation. In this paper, we explore how ontology can be further extended to handle user preference uncertainty by using a Bayesian network representation. Customer preference uncertainty is expressed as a probability of preference towards certain product attributes. An approach to construct a Bayesian network that harnesses the existing knowledge modeling from product family ontology is proposed. Based on such a network representation and preference modeling, we have derived several probabilistic measures to assess the propagation and impact of user preference uncertainty towards platform preference. A case study of platform analysis using four laptop computer families is reported to illustrate how preference uncertainty can affect the suitability and selection of existing product platform.

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.

Product Analysis and Variants Derivation Based on a Semantically Annotated Product Family Ontology

2009 ◽  
Author(s):  
Soon Chong Johnson Lim ◽  
Ying Liu ◽  
Wing Bun Lee
Keyword(s):  
Semantic Annotation ◽  
Product Family ◽  
Relevant Information ◽  
Product Analysis ◽  
Laptop Computer ◽  
Family Analysis ◽  
Design And Manufacturing ◽  
Commonality Analysis ◽  
Current Representation

In literature, there are a number of indexes suggested that serve as the indicator of commonality among product components, modules and variants. However, as these elements are increasingly interconnected with aspects other than the component view, the existing commonality metrics are unable to effectively model these aspects due to their limitation in capturing relevant information for analysis. Therefore, there exists a need to consider multiple design and manufacturing aspects in commonality metrics so that a comprehensive view of the commonality among product variants can be presented. In the current representation schemes proposed for product family modeling, ontology is one of the most promising ones to model the complex semantic relations among various elements in a product family. Nevertheless, the research and application of ontology in the analysis of a product family has so far received little attention. In this paper, we proposed a framework to generate a semantically annotated multi-facet product family ontology. Using a case study of a laptop computer family, we suggest and demonstrate a new commonality analysis approach based on the semantically annotated multi-facet laptop product family ontology. Together with a new method of deriving product variants based on the aforementioned ontology, our approach illustrates the merits of using semantic annotation in assisting ontology based product family analysis.

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.

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 The impact of information management in developing new products in bank – an empirical analysis

2021 ◽  
Vol 54 (3-4) ◽  
pp. 197-206
Author(s):  
Zoran Najdanović ◽  
Natalia Tutek
Keyword(s):  
Product Development ◽  
Information Management ◽  
New Product Development ◽  
New Products ◽  
New Product ◽  
Business Decisions ◽  
Customer Preferences ◽  
The Right ◽  
The Impact

Successful information management is big challenge for any organization. In this paper the emphasis is on information management in new product development in bank. Under strong pressure from competition and new technological changes, as well as the turbulent changes in the environment, financial institutions must continuously develop new products and services. In order to make the services more interesting to the users, it is necessary to collect data about the users, their wishes and preferences. The data should then be converted into useful information that will result with developing the right product or service that users will recognize as necessary. Products become personalized, user-friendly, and the emphasis is on the importance of long-term company relationships with customers. Only with well-organized information, managers can make the right business decisions and companies can react in time to market changes. When creating their strategy, successful companies analyze and identify elements that significantly contribute to creating a competitive advantage and ensuring long-term growth and development. The paper presents an empirical research of customer preferences which lead to new product development in bank.

scholarly journals Fuzzy based QoS prediction using bayesian network in cloud computing environment

2017 ◽  
Vol 7 (1.5) ◽  
pp. 170 ◽  
Author(s):  
Saravanan Chandrasekaran ◽  
Vijay Bhanu Srinivasan ◽  
Latha Parthiban
Keyword(s):  
Bayesian Network ◽  
User Satisfaction ◽  
Fuzzy Inference ◽  
User Preference ◽  
Inference Rules ◽  
Cloud Infrastructure ◽  
Qos Prediction ◽  
Prediction Probability ◽  
Service Capability ◽  
Novel Method

The Quality of Service (QoS) is enforced in discovering an optimal web service (WS).The QoS is uncertain due to the fluctuating performance of WS in the dynamic cloud environment. We propose a Fuzzy based Bayesian Network (FBN) system for Efficient QoS prediction. The novel method comprises three processes namely Semantic QoS Annotation, QoS Prediction, and Adaptive QoS using cloud infrastructure. The FBN employs the compliance factor to measure the performance of QoS attributes and fuzzy inference rules to infer the service capability. The inference rules are defined according to the user preference which assists to achieve the user satisfaction. The FBN returns the optimal WSs from a set of functionally equivalent WS. The unpredictable and extreme access of the selected WS is handled using cloud infrastructure. The results show that the FBN approach achieves nearly 95% of QoS prediction accuracy when providing an adequate number of past QoS data, and improves the prediction probability by 2.6% more than that of the existing approach.  

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