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Adaptive Sampling and Learning in Recommendation Systems


Abstract This thesis studies recommendation systems and considers joint sampling and learning. Sampling in recommendation systems is to obtain users' ratings on specific items chosen by the recommendation platform, and learning is to infer the unknown ratings of users to items given the existing data. In this thesis, the problem is formulated as an adaptive matrix completion problem in which sampling is to reveal the unknown entries of a $U\times M$ matrix where $U$ is the number of users, $M$ is the number of items, and each entry of the $U\times M$ matrix represents the rating of a user to an item. In the literature, this matrix completion problem has been studied under a static setting, i.e., recovering the matrix based on a set of partial ra... (more)
Created Date 2015
Contributor Zhu, Lingfang (Author) / Xue, Guoliang (Advisor) / He, Jingrui (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Subject Computer science
Type Masters Thesis
Extent 32 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis