Wenzel A Database Approach to Group Preference Problems in Social Networks and Geo-Rich Applications

1. Auflage 2016
ISBN: 978-3-7412-1191-1
Verlag: BoD - Books on Demand
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 194 Seiten

Smart Data is the new trend following the Big Data hype. The focus shifts from pure mass towards quality of data and the added value that data analysis can provide. To this end, Location-Based Social Networks (LBSN) are emerging as new area of application due to a vast amount of available personal information and the predominant demand for personalized user recommendations. In this social environment, personalization is extended from individual users to groups of users. Application scenarios include the recommendation of items to a group of users, recommending one user to another, or forming groups of users via recommendation of users to groups. As a work of applied computer science, the goal of this thesis is to systematically establish solutions for group preference problems of mentioned application scenarios. Technical challenges are met by following a database approach which is scalable to LBSN datasets of Big Data magnitude and allows for a fast computation of recommendation results. Semantic aspects are met by application of Preference SQL as database-driven preference framework which facilitates data-adaptive recommendations. By this design, user models can be formulated and evaluated on social network data and recommendations can be easily integrated into existing system architectures. The development of individual solutions requires extensions of Preference SQL towards geo-social domains and group preferences. For item-to-group recommendations, novel means for the individual statement of preferences as well as the formation of group preferences become of importance. Applications in user-to-user recommendation require data aggregation from different user profiles and subsequent preference analytics for the generation of extended implicit preferences. In addition, the definition of data-adaptive similarity measures based on social and psychological aspects of real-life interactions is a major factor. These concepts are leveraged for user-to-group scenarios to implement effective strategies for problem instances of large user sets. In the course of solving these practical application problems, superior theoretical questions emerged, such as heuristic algorithms for hyper-exponential solution spaces, that are also addressed by this work. The validity of proposed solutions is verified by demo applications and the implementation of semantic benchmarks.

Florian Wenzel was born in Bayreuth (Germany) in 1983. From 2003 to 2009 he studied Computer Science at the University of Würzburg, Germany and UT Austin, TX, USA Since 2009 he works as a researcher at the Chair for Databases and Information Systems at the University of Augsburg, Germany. His research topics include preferences in Location-Based Social Networks, graph databases, and mobile applications as well as group preference problems and recommendations. In 2015 he received his doctor's degree for the thesis presented in this book.

Autoren/Hrsg.

Wenzel, Florian

Inhaltsverzeichnis

1;Title Page;5
2;Copyright;6
3;Dedication;7
4;Table of Contents;9
5;Abstract;13
6;Acknowledgment;14
7;1 Introduction;15
7.1;1.1 Motivation;15
7.2;1.2 Contribution and Organization;18
8;2 Preference Overview;21
8.1;2.1 Basic Structures for Preference Modeling;22
8.2;2.2 Preferences in Economics;25
8.3;2.3 Preferences in Psychology;25
8.4;2.4 Preferences in Artificial Intelligence;27
8.5;2.5 Preferences in Database Systems;30
9;3 Preference SQL Background;33
9.1;3.1 Preference Algebra;34
9.2;3.2 Preference Constructors;36
9.2.1;3.2.1 Base Preferences;36
9.2.2;3.2.2 Complex Preferences;41
9.3;3.3 Domain-Specific Extensions;44
9.3.1;3.3.1 Spatial Preferences;45
9.3.2;3.3.2 Social Preferences;47
9.4;3.4 Group Preferences;57
9.5;3.5 Preference Query Evaluation;58
9.5.1;3.5.1 The BMO Query Model;59
9.5.2;3.5.2 Data-Adaptivity;59
9.6;3.6 The Preference SQL System;63
10;4 Group Recommendations;67
10.1;4.1 Area of Application;68
10.2;4.2 A Demo Application for Group Recommendations;70
10.3;4.3 Use Cases;74
10.4;4.4 Semantic Evaluation;76
10.5;4.5 Related Work;79
10.5.1;4.5.1 Decision Support Systems;80
10.5.2;4.5.2 Computational Social Choice;81
10.5.3;4.5.3 Spatial and Location-Dependent Skylines;82
10.5.4;4.5.4 Recommender Systems;83
11;5 User-to-User Recommendation in LBSN;87
11.1;5.1 Area of Application;88
11.2;5.2 Aggregation of Social Network Profiles;90
11.3;5.3 A User-to-User Recommendation Framework;93
11.3.1;5.3.1 Enriched User Models;93
11.3.2;5.3.2 Similarity Computation;95
11.3.3;5.3.3 Adaptation for Social Preferences;96
11.3.4;5.3.4 Benchmarks;97
11.4;5.4 Similarity Measures;99
11.5;5.5 Preference Analytics;103
11.5.1;5.5.1 User Profiles;103
11.5.2;5.5.2 Preference Generation;105
11.5.3;5.5.3 Benchmarks;110
11.6;5.6 Related Work;112
11.6.1;5.6.1 Friendship Recommendation;114
11.6.2;5.6.2 Preference and Profile Matching;115
11.6.3;5.6.3 Reciprocal Recommender Systems;116
12;6 Data-Adaptive Group Analysis in LBSN;119
12.1;6.1 Area of Application;120
12.2;6.2 Fundamentals of Group Formation;121
12.3;6.3 Solution Space Inspection Approach;123
12.3.1;6.3.1 Solution Space;123
12.3.2;6.3.2 Data-Adaptive Preference-Based Approach;124
12.3.3;6.3.3 Benchmarks;126
12.3.4;6.3.4 Demo Scenario;128
12.4;6.4 P-Means: Preference-Enhanced k-Means;132
12.4.1;6.4.1 User Representation;134
12.4.2;6.4.2 Centroid Generation and Computation;135
12.4.3;6.4.3 Cluster Assignment and Distance Measure;136
12.4.4;6.4.4 Satisfaction of Hard Constraints;136
12.4.5;6.4.5 Benchmarks;137
12.5;6.5 Preference-Based MIN-K Algorithm;139
12.5.1;6.5.1 Group Similarity;140
12.5.2;6.5.2 Seed Selection;141
12.5.3;6.5.3 User Allocation;141
12.5.4;6.5.4 Benchmarks;144
12.6;6.6 Semantic Benchmarks;145
12.6.1;6.6.1 User Generation;146
12.6.2;6.6.2 Evaluation Criteria;147
12.6.3;6.6.3 Evaluation Results;148
12.7;6.7 Related Work;150
12.7.1;6.7.1 Clustering;150
12.7.2;6.7.2 Community Detection;152
12.7.3;6.7.3 Matching under Preferences;152
13;7 Conclusion;155
13.1;7.1 Summary;156
13.2;7.2 Future Work;157
14;A Algebraic Laws for Group Preferences;159
15;B Definition of User Stereotypes;167
16;Bibliography;171
17;List of Figures;187
18;List of Tables;189
19;List of Algorithms;191
20;Curriculum Vitae;193