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Permanent link (DOI): https://doi.org/10.7939/R32Z12X89

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Separating-Plane Factorization Models: Scalable Recommendation from One-class Implicit Feedback Open Access

Descriptions

Other title
Subject/Keyword
Separating-plane
Matrix factorization
Factorization machine
Big data
Recommender system
Implicit feedback
One-class problem
Spark
Tencent
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Chen, Haolan
Supervisor and department
Niu, Di (ECE)
Ardakani, Masoud (ECE)
Examining committee member and department
Dick, Scott (ECE)
Kong, Linglong (Statistics)
Niu, Di (ECE)
Ardakani, Masoud (ECE)
Department
Department of Electrical and Computer Engineering
Specialization
Software Engineering & Intelligent Systems
Date accepted
2016-09-29T13:05:37Z
Graduation date
2016-06:Fall 2016
Degree
Master of Science
Degree level
Master's
Abstract
We study the large-scale video recommendation problem based on user viewing logs instead of explicit ratings. As viewing records are implicitly positive samples, existing matrix factorization methods fail to generate discriminative recommendations based on such one-class data. We propose a scalable approach called separating-plane matrix factorization (SPMF) to make effective recommendations based on one-class implicit feedback, with a learning complexity only comparable to matrix factorization. With extensive offline evaluation in Tencent Data Warehouse (TDW) based on big data, we show that our approach outperforms a wide range of state-of-the-art methods. We also deployed our system online to test with real users in Tencent QQ Browser mobile app. Results show that our approach can increase the video click through rate by 23% over implicit-feedback collaborative filtering (IFCF), a scheme implemented in Spark’s MLlib.
Language
English
DOI
doi:10.7939/R32Z12X89
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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