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Trust-based Collective View Prediction

Author: Tiejian Luo; Su Chen; Guandong Xu; Jia Zhou
Publisher: Dordrecht : Springer, 2013.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users' past behaviors. Still, these  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Luo, Tiejian.
Trust-based Collective View Prediction.
Dordrecht : Springer, ©2013
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Tiejian Luo; Su Chen; Guandong Xu; Jia Zhou
ISBN: 9781461472025 1461472024
OCLC Number: 854976099
Notes: 5.3.3 Indicator Systems.
Description: 1 online resource (150 pages)
Contents: Preface; Contents; 1 Introduction; 1.1 ... Background; 1.2 ... Research Theme; 1.3 ... Scope and Strategy; 1.4 ... Contributions; 2 Related Work; 2.1 ... Recommendation Algorithm; 2.1.1 Content-Based Recommendation; 2.1.2 Collaborative Filtering; 2.1.3 Hybrid Methods; 2.2 ... Sentiment Analysis; 2.3 ... Dynamic Network Mechanism; 2.3.1 Statistic Characteristics; 2.3.1.1 Long Tail Distribution; 2.3.1.2 Small Diameter; 2.3.1.3 Clustering Effect; 2.3.1.4 Other Characteristics; 2.3.2 Evolution Law; 2.3.2.1 Evolution Model; 2.3.2.2 Link Prediction; 2.3.3 Web Information Cascades; 2.4 ... Trust Management; 2.5 ... Summary. 3 Collaborative Filtering3.1 ... Neighborhood-Based Method; 3.1.1 User-Based CF; 3.1.2 Item-Based CF; 3.1.3 Comprehensive Analysis of User-Based CF and Item-Based CF; 3.2 ... Latent Factor Model; 3.2.1 Singular Value Decomposition; 3.2.2 Regularized Singular Value Decomposition; 3.3 ... Graph-Based Collaborative Filtering; 3.3.1 Bipartite Graph Model of Collaborative Filtering; 3.3.2 Graph-Based algorithm of Collaborative Filtering; 3.4 ... Socialization Collaborative Filtering; 3.4.1 Gathering Socialization Data; 3.4.1.1 User Registration Information; 3.4.1.2 User Location Information; 3.4.1.3 Forum. 3.4.1.4 Instant Message Tool3.4.1.5 Social Network; 3.4.2 Neighborhood-Based Socialization Recommendation Algorithm; 3.4.3 Graph-Based Socialization Recommendation Algorithm; 3.5 ... Dynamic Model in Collaborative Filtering; 3.5.1 Dynamic Neighborhood-Based Model; 3.5.2 Dynamic Latent Factor Model; 3.5.2.1 Time Bias; 3.5.2.2 User Bias Shifting; 3.5.2.3 Item Biases Shifting; 3.5.2.4 User Preference Shifting; 3.5.3 Case Study: Dynamic Graph-Based Collaborative Filtering; 3.5.3.1 Introduction; 3.5.3.2 Related Work; 3.5.3.3 Methodology; 3.5.3.4 Experiments; 3.6 ... Conclusion; 4 Sentiment Analysis. 4.1 ... Sentiment Identification4.1.1 Dictionary-Based OpinionOpinion Words Generation; 4.1.2 Corpus-Based OpinionOpinion Words Generation; 4.2 ... Sentiment Orientation Classification; 4.2.1 Counting OpinionOpinion Words; 4.2.2 Supervised Learning Approaches; 4.3 ... Case Study: Sentimental Analysis in Recommender Systems; 4.3.1 Introduction; 4.3.2 Related Work; 4.3.2.1 Tag-Based Personalized RecommendationPersonalized Recommendation; 4.3.2.2 Sentiment Analysis; 4.3.3 Preliminaries; 4.3.3.1 Social Tagging System Model; 4.3.3.2 Standard Tag-Based Recommendation. 4.3.4 Sentiment Enhanced Approach for Tag-Based Recommendation4.3.4.1 Sentiment Enhanced Approach; 4.3.4.2 Running Example; 4.3.5 Experimental Evaluation; 4.3.5.1 Experimental Data Set; 4.3.5.2 Data Set Preprocessing; 4.3.5.3 Evaluation Methodology and Results; 5 Theoretical Foundations; 5.1 ... Traditional Prediction Method; 5.2 ... Trust and Its Measurement; 5.2.1 Data Source; 5.2.2 Statistical and Transmission Characteristics; 5.3 ... Analysis of Trust and Collective View; 5.3.1 Dataset; 5.3.2 Measurement; 5.3.2.1 Similarity Measure; 5.3.2.2 Trust Statement and Trust Function; 5.3.2.3 Trust Inference.

Abstract:

Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users' past behaviors. Still, these techniques are vulnerable to the artificially-injected noise data, because they are not able to judge the reliability and credibility of the information sources. Trust-based Collective View.

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