Modeling User Preference from Rating Data Based on the Bayesian Network with a Latent Variable

2016 ◽  
pp. 3-16 ◽  
Author(s):  
Renshang Gao ◽  
Kun Yue ◽  
Hao Wu ◽  
Binbin Zhang ◽  
Xiaodong Fu
Keyword(s):  
Bayesian Network ◽  
Latent Variable ◽  
User Preference ◽  
Rating Data

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.  

A parallel and constraint induced approach to modeling user preference from rating data

2020 ◽  
Vol 204 ◽  
pp. 106206
Author(s):  
Kun Yue ◽  
Xinran Wu ◽  
Liang Duan ◽  
Shaojie Qiao ◽  
Hao Wu
Keyword(s):  
User Preference ◽  
Rating Data

scholarly journals A Personalized Music Recommender System Using User Contents, Music Contents and Preference Ratings

2020 ◽  
Vol 07 (01) ◽  
pp. 77-92
Author(s):  
Ja-Hwung Su ◽  
Chu-Yu Chin ◽  
Yi-Wen Liao ◽  
Hsiao-Chuan Yang ◽  
Vincent S. Tseng ◽  
...  
Keyword(s):  
Recommender System ◽  
Mean Squared Error ◽  
Communication Technologies ◽  
User Preference ◽  
Acoustic Features ◽  
Rating Data ◽  
The Arts ◽  
Online Music ◽  
Preference Ratings ◽  
Better Than

Recently, the advances in communication technologies have made music retrieval easier. Without downloading the music, the users can listen to music through online music websites. This incurs a challenging issue of how to provide the users with an effective online listening service. Although a number of past studies paid attention to this issue, the problems of new user, new item and rating sparsity are not easy to solve. To deal with these problems, in this paper, we propose a novel music recommender system that fuses user contents, music contents and preference ratings to enhance the music recommendation. For dealing with problem of new user, the user similarities are calculated by user profiles instead of traditional ratings. By the user similarities, the unknown ratings can be predicted using user-based Collaborative Filtering (CF). For dealing with problems of rating sparsity and new items, the unknown ratings are initialized by acoustic features and music genre ratings. Because the unknown ratings are initially imputed, the rating data will be enriched. Thereupon, the user preference can be predicted effectively by item-based CF. The evaluation results show that our proposed music recommender system performs better than the state-of-the-arts methods in terms of Root Mean Squared Error.

Dynamic Bayesian network for robust latent variable modeling and fault classification

2020 ◽  
Vol 89 ◽  
pp. 103475 ◽  
Author(s):  
Junhua Zheng ◽  
Jinlin Zhu ◽  
Guangjie Chen ◽  
Zhihuan Song ◽  
Zhiqiang Ge
Keyword(s):  
Bayesian Network ◽  
Latent Variable ◽  
Dynamic Bayesian Network ◽  
Fault Classification ◽  
Latent Variable Modeling

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 Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Li ◽  
Yan Guo
Keyword(s):  
Big Data ◽  
High Performance ◽  
Cultural Distance ◽  
Edge Computing ◽  
User Preference ◽  
Mobile Edge Computing ◽  
Service Recommendation ◽  
Rating Data ◽  
Close Proximity ◽  
Real World Datasets

In the era of big data, traditional computing systems and paradigms are not efficient and even difficult to use. For high performance big data processing, mobile edge computing is emerging as a complement framework of cloud computing. In this new computing architecture, services are provided within a close proximity of mobile users by servers at the edge of network. Traditional collaborative filtering recommendation approach only focuses on the similarity extracted from the rating data, which may lead to an inaccuracy expression of user preference. In this paper, we propose a cultural distance-aware service recommendation approach which focuses on not only the similarity but also the local characteristics and preference of users. Our approach employs the cultural distance to express the user preference and combines it with similarity to predict the user ratings and recommend the services with higher rating. In addition, considering the extreme sparsity of the rating data, missing rating prediction based on collaboration filtering is introduced in our approach. The experimental results based on real-world datasets show that our approach outperforms the traditional recommendation approaches in terms of the reliability of recommendation.

scholarly journals Implicit Feedback Recommendation Method Based on User-Generated Content

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bing Fang ◽  
Enpeng Hu ◽  
Junyang Shen ◽  
Jingwen Zhang ◽  
Yang Chen
Keyword(s):  
Large Scale ◽  
Semantic Analysis ◽  
User Preferences ◽  
User Preference ◽  
User Generated Content ◽  
Implicit Feedback ◽  
Rating Data ◽  
Customer Needs ◽  
Sparsity Problem ◽  
Customer Need

Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

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