A Multi-objective Framework for Location Recommendation Based on User Preference

Shanfeng Wang, Maoguo Gong, Can Qin, Junwei Yang
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引用次数: 6

Abstract

Location recommendation has attracted increasing attention in recent years. This paper proposes a novel multi-objective framework for location recommendation based on user preference. Under this framework, user preference can be separated into common preference and individual preference. Then two contradictory objective functions are designed to describe these two kinds of preferences. It is difficult to optimize these two objective functions simultaneously. In this paper, a novel multi-objective evolutionary algorithm is proposed to optimize these two objective functions. The proposed algorithm can make a good balance between these two objective functions. Experiments on two real application recommendation scenarios: Foursquare dataset and Gowalla dataset show that the proposed algorithm is effective to recommend locations.
基于用户偏好的多目标位置推荐框架
近年来,位置推荐越来越受到人们的关注。提出了一种新的基于用户偏好的多目标位置推荐框架。在此框架下,用户偏好可以分为共同偏好和个人偏好。然后设计了两个相互矛盾的目标函数来描述这两种偏好。这两个目标函数很难同时优化。本文提出了一种新的多目标进化算法来优化这两个目标函数。该算法能很好地平衡这两个目标函数。在Foursquare数据集和Gowalla数据集两种真实应用推荐场景下的实验表明,本文算法对地点推荐是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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