计算机科学
人工智能
群(周期表)
图形
建筑
推荐系统
兴趣点
完备性(序理论)
机器学习
比例(比率)
人机交互
数据挖掘
理论计算机科学
量子力学
视觉艺术
艺术
数学分析
化学
物理
数学
有机化学
作者
Sayda Elmi,Kian-Lee Tan
标识
DOI:10.1007/978-3-031-39821-6_24
摘要
A significant amount of data are significantly available due to the dramatic proliferation of location-based social networks. This has led to the development of location-based recommendation tools that assist users in discovering attractive Points-of-Interest (POIs). Next POIs recommendation is of great importance for not only individuals but also group of users since group activities have become an integral part of our daily life. However, most existing methods make recommendations through aggregating individual predictive results rather than considering the collective features that govern user preferences made within a group. This insufficiency can directly affect the completeness and semantic accuracy of group features. For this reason, we propose a novel approach which accommodates both individual preferences and group decisions in a joint model. More specifically, based on influencing users in a group, we devise a hybrid deep architecture model built with graph convolution networks and attention mechanism to extract connections between group and personal preferences and then capture the impact of each user on the group decision-making, respectively. We conduct extensive experiments to evaluate the performance of our model on two well-known real large-scale datasets, namely, Gowalla and Foursquare.
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