Muutke küpsiste eelistusi

Social Network-Based Recommender Systems 1st ed. 2015 [Kõva köide]

4.50/5 (4 hinnangut Goodreads-ist)
  • Formaat: Hardback, 126 pages, kõrgus x laius: 235x155 mm, kaal: 3376 g, XIII, 126 p., 1 Hardback
  • Ilmumisaeg: 01-Oct-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319227343
  • ISBN-13: 9783319227344
Teised raamatud teemal:
  • Kõva köide
  • Hind: 48,70 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 57,29 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
Social Network-Based Recommender Systems 1st ed. 2015Suurem pilt
  • Formaat: Hardback, 126 pages, kõrgus x laius: 235x155 mm, kaal: 3376 g, XIII, 126 p., 1 Hardback
  • Ilmumisaeg: 01-Oct-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319227343
  • ISBN-13: 9783319227344
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319227344.html
Märksõnad:
This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on "social brokers" are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will

also find this books useful as a secondary text.

Overview of Social Recommender Systems.- Link Prediction for Directed Graphs.- Follow Recommendation in Communities.- Partner Recommendation.- Social Broker Recommendation.- Conclusion.

Arvustused

The book is quite brief. It contains a lot of rather technical information concentrated around particular topics. I highly recommend this book to students, professionals, experts, and others interested in the potential of recommendations taking place within social networks. (P. Navrat, Computing Reviews, computingreviews.com, June, 2016)

1 Overview Social Recommender Systems
1(6)
1.1 Recommendations in Social Networks
1(1)
1.2 Recommendation Techniques
2(3)
1.2.1 Link Prediction
3(1)
1.2.2 Follow Recommendation
3(1)
1.2.3 Partner Recommendation
4(1)
1.2.4 Broker Recommendation
4(1)
1.3 Research Datasets
5(1)
1.4 Book Outline
5(2)
References
6(1)
2 Link Prediction for Directed Graphs
7(26)
2.1 Friendship Recommendation
7(2)
2.2 Background in Link Prediction
9(1)
2.3 Software Framework
10(5)
2.4 Predicting Friendship
15(5)
2.4.1 Similarity-Based Metrics
15(1)
2.4.2 Triad Patterns
15(2)
2.4.3 Triadic Closeness
17(1)
2.4.4 Triadic Closeness Example
18(2)
2.5 Data Collection
20(2)
2.6 Evaluation
22(6)
2.6.1 Configuration
22(2)
2.6.2 Prediction Results
24(4)
2.7 Conclusions
28(5)
References
29(4)
3 Follow Recommendation in Communities
33(26)
3.1 Social Collaboration Platforms
33(2)
3.2 Background in Online Communities
35(1)
3.3 Recommendation Types
36(1)
3.4 Follow Recommendation
37(6)
3.4.1 Authority-Based Recommendation Model
37(2)
3.4.2 Personalized Authority Ranking
39(3)
3.4.3 Weights and Personalization Metrics
42(1)
3.5 Data Collection
43(7)
3.5.1 GitHub Community
43(2)
3.5.2 User Activity by Location
45(2)
3.5.3 Programming Languages
47(1)
3.5.4 Follower Graph and Reciprocity
48(2)
3.6 Evaluation
50(5)
3.6.1 Recommendations Without Personalization
51(1)
3.6.2 Personalized Recommendations
52(3)
3.7 Conclusions
55(4)
References
56(3)
4 Partner Recommendation
59(36)
4.1 Social Network-Based Collaboration
59(1)
4.2 Background in Strategic Formation
60(2)
4.3 Reputation Model
62(5)
4.3.1 Basic Definitions
62(1)
4.3.2 Hubs and Authorities
63(1)
4.3.3 Query-Sensitive Personalization
64(1)
4.3.4 Time-Aware Authority
65(2)
4.4 Structural Importance Model
67(3)
4.5 Framework and Ranking Algorithm
70(5)
4.5.1 Software Framework
70(3)
4.5.2 Ranking Algorithm
73(2)
4.6 Evaluation
75(11)
4.6.1 Description of Dataset
75(2)
4.6.2 Top-k Rank Evaluation
77(6)
4.6.3 Statistical Comparison
83(3)
4.7 Conclusions
86(9)
References
92(3)
5 Social Broker Recommendation
95(30)
5.1 Virtual Organizations
95(2)
5.2 Background in Distributed Organizations
97(2)
5.3 Hybrid Compute Environment
99(3)
5.3.1 Human-Provided Services
99(1)
5.3.2 Emergence and Evolution of Social Trust
100(2)
5.4 Expert Communities
102(3)
5.4.1 Collaboration Scenario
102(1)
5.4.2 Brokerage and Composition
103(1)
5.4.3 Broker Patterns and Policies
104(1)
5.5 SBQL Syntax
105(4)
5.5.1 Connecting Predefined Communities
106(2)
5.5.2 Finding Communities
108(1)
5.5.3 Finding Exclusive Brokers
109(1)
5.6 Broker Ranking
109(6)
5.6.1 Community Profiles
109(2)
5.6.2 Trust Weights
111(1)
5.6.3 Broker Importance
111(4)
5.7 Evaluation
115(6)
5.7.1 Overview
115(1)
5.7.2 Performance Tests
116(2)
5.7.3 Ranking Experiments
118(3)
5.7.4 Lessons Learned
121(1)
5.8 Conclusions
121(4)
References
122(3)
6 Conclusion
125