Preference Mining Using Neighborhood Rough Set Model on Two Universes

Abstract

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.

An Improved Fish Swarm Algorithm for Neighborhood Rough Set Reduction and its Application

“…When dealing with continuous data, discretization must be done beforehand, which inevitably leads to information lost [4]. To overcome this disadvantage, many extension of RST such as fuzzy rough sets [5], tolerance approximation model [6], covering approximation mode [7] and neighborhood rough set model [8], [9] have been proposed. Among which, the neighborhood rough set model can process both numerical and categorical dataset via the δ-neighborhood set.…”

Kernel Neighborhood Rough Sets Model and Its Application

“…This theory has been successfully applied to many fields, such as data mining, decision-making, pattern recognition, machine learning, and intelligent control [1][2][3][4]. Kernel rough sets [5] and neighborhood rough sets [6] are two important models in rough set theory.…”