Bayesian pairwise learning to rank via one-class collaborative filtering

Wang Zhou; Jianping Li; Yongluan Zhou; Muhammad Hammad Memon

doi:10.1016/j.neucom.2019.08.027

Bayesian pairwise learning to rank via one-class collaborative filtering

Wang Zhou^*, Jianping Li, Yongluan Zhou, Muhammad Hammad Memon

^*Corresponding author for this work

6 Citations (Scopus)

Abstract

With the ever-growing scale of social websites and online transactions, in past decade, Recommender System (RS) has become a crucial tool to overcome information overload, due to its powerful capability in information filtering and retrieval. Traditional rating prediction based RS could learn user's preference according to the explicit feedback, however, such numerical user-item ratings are always unavailable in real life. By contrast, pairwise learning algorithms could directly optimize for ranking and provide personalized recommendation from implicit feedback, although suffering from such data sparsity and slow convergence problems. Motivated by these, in this article, a novel collaborative pairwise learning to rank method referred to as BPLR is proposed, which aims to improve the performance of personalized ranking from implicit feedback. To this end, BPLR tries to partition items into positive feedback, potential feedback and negative feedback, and takes account of the neighborhood relationship between users as well as the item similarity while deriving the potential candidates, moreover, a dynamic sampling strategy is designed to reduce the computational complexity and speed up model training. Empirical experiments over four real world datasets certificate the effectiveness and efficiency of BPLR, which could speed up convergence, and outperform state-of-the-art algorithms significantly in personalized top-N recommendation.

Original language	English
Journal	Neurocomputing
Volume	367
Pages (from-to)	176-187
ISSN	0925-2312
DOIs	https://doi.org/10.1016/j.neucom.2019.08.027
Publication status	Published - 20 Nov 2019

Keywords

Dynamic sampling
Pairwise learning
Personalized ranking
Potential feedback

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10.1016/j.neucom.2019.08.027

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title = "Bayesian pairwise learning to rank via one-class collaborative filtering",

abstract = "With the ever-growing scale of social websites and online transactions, in past decade, Recommender System (RS) has become a crucial tool to overcome information overload, due to its powerful capability in information filtering and retrieval. Traditional rating prediction based RS could learn user's preference according to the explicit feedback, however, such numerical user-item ratings are always unavailable in real life. By contrast, pairwise learning algorithms could directly optimize for ranking and provide personalized recommendation from implicit feedback, although suffering from such data sparsity and slow convergence problems. Motivated by these, in this article, a novel collaborative pairwise learning to rank method referred to as BPLR is proposed, which aims to improve the performance of personalized ranking from implicit feedback. To this end, BPLR tries to partition items into positive feedback, potential feedback and negative feedback, and takes account of the neighborhood relationship between users as well as the item similarity while deriving the potential candidates, moreover, a dynamic sampling strategy is designed to reduce the computational complexity and speed up model training. Empirical experiments over four real world datasets certificate the effectiveness and efficiency of BPLR, which could speed up convergence, and outperform state-of-the-art algorithms significantly in personalized top-N recommendation.",

keywords = "Dynamic sampling, Pairwise learning, Personalized ranking, Potential feedback",

author = "Wang Zhou and Jianping Li and Yongluan Zhou and Memon, {Muhammad Hammad}",

year = "2019",

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doi = "10.1016/j.neucom.2019.08.027",

language = "English",

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T1 - Bayesian pairwise learning to rank via one-class collaborative filtering

AU - Zhou, Wang

AU - Li, Jianping

AU - Zhou, Yongluan

AU - Memon, Muhammad Hammad

PY - 2019/11/20

Y1 - 2019/11/20

N2 - With the ever-growing scale of social websites and online transactions, in past decade, Recommender System (RS) has become a crucial tool to overcome information overload, due to its powerful capability in information filtering and retrieval. Traditional rating prediction based RS could learn user's preference according to the explicit feedback, however, such numerical user-item ratings are always unavailable in real life. By contrast, pairwise learning algorithms could directly optimize for ranking and provide personalized recommendation from implicit feedback, although suffering from such data sparsity and slow convergence problems. Motivated by these, in this article, a novel collaborative pairwise learning to rank method referred to as BPLR is proposed, which aims to improve the performance of personalized ranking from implicit feedback. To this end, BPLR tries to partition items into positive feedback, potential feedback and negative feedback, and takes account of the neighborhood relationship between users as well as the item similarity while deriving the potential candidates, moreover, a dynamic sampling strategy is designed to reduce the computational complexity and speed up model training. Empirical experiments over four real world datasets certificate the effectiveness and efficiency of BPLR, which could speed up convergence, and outperform state-of-the-art algorithms significantly in personalized top-N recommendation.

AB - With the ever-growing scale of social websites and online transactions, in past decade, Recommender System (RS) has become a crucial tool to overcome information overload, due to its powerful capability in information filtering and retrieval. Traditional rating prediction based RS could learn user's preference according to the explicit feedback, however, such numerical user-item ratings are always unavailable in real life. By contrast, pairwise learning algorithms could directly optimize for ranking and provide personalized recommendation from implicit feedback, although suffering from such data sparsity and slow convergence problems. Motivated by these, in this article, a novel collaborative pairwise learning to rank method referred to as BPLR is proposed, which aims to improve the performance of personalized ranking from implicit feedback. To this end, BPLR tries to partition items into positive feedback, potential feedback and negative feedback, and takes account of the neighborhood relationship between users as well as the item similarity while deriving the potential candidates, moreover, a dynamic sampling strategy is designed to reduce the computational complexity and speed up model training. Empirical experiments over four real world datasets certificate the effectiveness and efficiency of BPLR, which could speed up convergence, and outperform state-of-the-art algorithms significantly in personalized top-N recommendation.

KW - Dynamic sampling

KW - Pairwise learning

KW - Personalized ranking

KW - Potential feedback

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DO - 10.1016/j.neucom.2019.08.027

M3 - Journal article

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SN - 0925-2312

VL - 367

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EP - 187

JO - Neurocomputing

JF - Neurocomputing

ER -

Bayesian pairwise learning to rank via one-class collaborative filtering

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Keywords

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