Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85173
Title: BPRH : bayesian personalized ranking for heterogeneous implicit feedback
Authors: Qiu, Huihuai
Liu, Yun
Guo, Guibing
Sun, Zhu
Zhang, Jie
Nguyen, Hai Thanh
Keywords: Recommendation
Heterogeneous Implicit Feedback
Engineering::Computer science and engineering
Issue Date: 2018
Source: Qiu, H., Liu, Y., Guo, G., Sun, Z., Zhang, J., & Nguyen, H. T. (2018). BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Information Sciences, 453, 80-98. doi:10.1016/j.ins.2018.04.027
Series/Report no.: Information Sciences
Abstract: Personalized recommendation for online service systems aims to predict potential demand by analysing user preference. User preference can be inferred from heterogeneous implicit feedback (i.e. various user actions) especially when explicit feedback (i.e. ratings) is not available. However, most methods either merely focus on homogeneous implicit feedback (i.e. target action), e.g., purchase in shopping websites and forward in Twitter, or dispose heterogeneous implicit feedback without the investigation of its speciality. In this paper, we adopt two typical actions in online service systems, i.e., view and like, as auxiliary feedback to enhance recommendation performance, whereby we propose a Bayesian personalized ranking method for heterogeneous implicit feedback (BPRH). Specifically, items are first classified into different types according to the actions they received. Then by analysing the co-occurrence of different types of actions, which is one of the fundamental speciality of heterogeneous implicit feedback systems, we quantify their correlations, based on which the difference of users’ preference among different types of items is investigated. An adaptive sampling strategy is also proposed to tackle the unbalanced correlation among different actions. Extensive experimentation on three real-world datasets demonstrates that our approach significantly outperforms state-of-the-art algorithms.
URI: https://hdl.handle.net/10356/85173
http://hdl.handle.net/10220/49178
ISSN: 0020-0255
DOI: 10.1016/j.ins.2018.04.027
Rights: © 2018 Elsevier Inc. All rights reserved. This paper was published in Information Sciences and is made available with permission of Elsevier Inc.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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