Joint Attention Networks with Inherent and Contextual Preference-Awareness for Successive POI Recommendation
Joint Attention Networks with Inherent and Contextual Preference-Awareness for Successive POI Recommendation
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DOI：
10.1007/s41019-022-00199-z
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发表时间：
2022-10
影响因子：
4.2
通讯作者：
Haiting Zhong;W. He;Li-zhen Cui;Lei Liu;Zhongmin Yan;Kun Zhao
中科院分区：
文献类型：
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作者：
Haiting Zhong;W. He;Li-zhen Cui;Lei Liu;Zhongmin Yan;Kun Zhao
如今，在互联网上使用移动的设备记录和共享个人生活变得越来越流行，并且连续POI推荐正获得学术界和工业界越来越多的关注。在移动的场景中，用户偏好的多样性、用户行为的多变性和时空背景的动态性等多重影响因素给兴趣点推荐系统带来了巨大的挑战。为了在动态环境下准确地捕获移动的用户的稳定偏好和上下文偏好，提出了一种基于内在偏好和上下文偏好的联合注意力网络（Joint Attention Networks with Inherent and Contextual Preferences，JANICP）的兴趣点推荐方法，该方法通过联合训练离线/近线用户内在兴趣感知模型和在线用户上下文兴趣预测模型，实现兴趣点推荐。离线模型基于全局历史行为数据进行训练，实现稳定的兴趣表示;在线模型基于即时选取的上下文敏感数据进行训练，实现动态的兴趣感知。注意力聚集和匹配模块用于将这两种偏好表示完全连接起来，并生成最终的POI推荐。在三个真实的数据集上进行了大量的实验，实验结果表明，所提出的JANICP优于现有的最先进的方法。
Nowadays recording and sharing personal lives using mobile devices on the Internet is becoming increasingly popular, and successive POI recommendation is gaining growing attention from academia and industry. In mobile scenarios, multiple influencing factors including the diversity of user preferences, the changeability of user behavior and the dynamic of spatiotemporal context bring great challenges to the POI recommender system. In order to accurately capture both the stable and the contextual preferences of mobile users in dynamic contexts, we propose a fusion framework JANICP (Joint Attention Networks with Inherent and Contextual Preferences) for successive POI recommendation by jointly training an offline/nearline user inherent interest perception model and an online user contextual interest prediction model. The offline model is trained based on the global historical behavior data to achieve stable interest representation, while the online model is trained based on the instantly selected context-sensitive data to achieve dynamic interest perception. An attention aggregation and matching module is used to fully connect the two kinds of preference representations and generate the final POI recommendation. Extensive experiments were conducted on three real datasets and experimental results show that the proposed JANICP outperforms existing state-of-the-art methods.