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Nonparametric Predictions for Network Links and Recommendation Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nonparametric Predictions for Network Links and Recommendation Systems./
作者:	Lu, Jiashen.
面頁冊數:	1 online resource (90 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-08, Section: A.
Contained By:	Dissertations Abstracts International84-08A.
標題:	Sensitivity analysis. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30204272click for full text (PQDT)
ISBN:	9798371937674

Nonparametric Predictions for Network Links and Recommendation Systems.
Lu, Jiashen.

Nonparametric Predictions for Network Links and Recommendation Systems. - 1 online resource (90 pages)

Source: Dissertations Abstracts International, Volume: 84-08, Section: A.

Thesis (Ph.D.)--University of Pittsburgh, 2022.

Includes bibliographical references

In this thesis, we develop methodologies to make nonparametric predictions in relational data. Prominent examples of relational data include user-user network interactions and user-item recommendation systems. For social networks, we follow a new latent position framework and develop prediction methods in pure cold-start scenarios where the new nodes do not have any observed links to start with. For recommendation systems, we first develop a Zero-imputation method to address the challenges of heterogeneous missing and then make predictions for missing values and for new users or items. We explore some applications of this Zero-imputation method in the context of social network with missing edges. In particular, we are interested in inferences in network regression models. We compare our approach with existing methods through simulations and apply our method to one real Friends and Lifestyle data that study the influence of social network on alcohol and drug use behaviors among teenagers.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798371937674Subjects--Topical Terms:

3560752
Sensitivity analysis.
Index Terms--Genre/Form:

542853
Electronic books.

Nonparametric Predictions for Network Links and Recommendation Systems.
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500 $a Source: Dissertations Abstracts International, Volume: 84-08, Section: A.
500 $a Advisor: Lei, Jing; Mentch, Lucas; Iyengar, Satish; Chen, Kehui.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2022.
504 $a Includes bibliographical references
520 $a In this thesis, we develop methodologies to make nonparametric predictions in relational data. Prominent examples of relational data include user-user network interactions and user-item recommendation systems. For social networks, we follow a new latent position framework and develop prediction methods in pure cold-start scenarios where the new nodes do not have any observed links to start with. For recommendation systems, we first develop a Zero-imputation method to address the challenges of heterogeneous missing and then make predictions for missing values and for new users or items. We explore some applications of this Zero-imputation method in the context of social network with missing edges. In particular, we are interested in inferences in network regression models. We compare our approach with existing methods through simulations and apply our method to one real Friends and Lifestyle data that study the influence of social network on alcohol and drug use behaviors among teenagers.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Sensitivity analysis. $3 3560752
650 4 $a Recommender systems. $3 3562220
650 4 $a Optimization. $3 891104
650 4 $a Decomposition. $3 3561186
650 4 $a Probability. $3 518898
650 4 $a Eigenvalues. $3 631789
650 4 $a Algorithms. $3 536374
650 4 $a Eigenvectors. $3 836550
650 4 $a Lifestyles. $3 557007
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773 0 $t Dissertations Abstracts International $g 84-08A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30204272 $z click for full text (PQDT)