A Rough Sets Approach to User Preference Modeling

Jing, Siyuan; She, Kun

doi:10.1007/978-3-642-16248-0_50

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2016

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“…This theory has been successfully applied to many fields, such as data mining, decision making, pattern recognizing, machine learning, and intelligent controlling [26–29]. …”

Section: Related Workmentioning

confidence: 99%

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Preference Mining Using Neighborhood Rough Set Model on Two Universes

Zeng

2016

Computational Intelligence and Neuroscience

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Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method.

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“…This theory has been successfully applied to many fields, such as data mining, decision making, pattern recognizing, machine learning, and intelligent controlling [26–29]. …”

Section: Related Workmentioning

confidence: 99%

“…Most of the researches have been conducted on the assumption of the same universe [26–29]. However, two-universe model is more appropriate for the preference mining problem.…”

Section: Related Workmentioning

confidence: 99%

Preference Mining Using Neighborhood Rough Set Model on Two Universes

Zeng

2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

A Rough Sets Approach to User Preference Modeling

Cited by 1 publication

References 8 publications

Preference Mining Using Neighborhood Rough Set Model on Two Universes

Preference Mining Using Neighborhood Rough Set Model on Two Universes

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