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Nonparametric Predictions for Network Links and Recommendation Systems

Lu, Jiashen (2022) Nonparametric Predictions for Network Links and Recommendation Systems. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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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.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Lu, Jiashenjil235@pitt.edujil235
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChen,
Committee MemberIyengar,
Committee MemberMentch,
Committee MemberLei,
Date: 12 October 2022
Date Type: Publication
Defense Date: 28 April 2022
Approval Date: 12 October 2022
Submission Date: 12 July 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 89
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Link predictions, Graph Root Distribution, Bipartite Graph, Cold-Start, Missing Imputation, Network AutoRegressive Model
Date Deposited: 12 Oct 2022 15:13
Last Modified: 12 Oct 2022 15:13


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