兴趣点
计算机科学
背景(考古学)
公制(单位)
代表(政治)
期限(时间)
空间语境意识
偏爱
人工智能
数据挖掘
点(几何)
机器学习
情报检索
地理
运营管理
物理
几何学
数学
考古
量子力学
政治
政治学
法学
经济
微观经济学
作者
Malika Acharya,Krishna Kumar Mohbey,Dharmendra Singh Rajput
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:12: 11584-11596
被引量:1
标识
DOI:10.1109/access.2024.3354934
摘要
The growth of the tourism industry has greatly boosted the Point-of-Interest (POI) recommendation tasks using Location-based Social Networks (LBSNs). The ever-evolving nature of user preferences poses a major problem. To address this, we propose a Long-Term Preference Mining (LTPM) approach that utilizes the Temporal Recency (TR) measure in the visits along with the location-aware recommendation based on Spatial Proximity (SP) to the user’s location. The temporal dynamics and changing preferences are exploited based on the modified Long Short-term Memory (LSTM) that utilizes the time decay. The spatial considerations are modeled in two aspects: geographical proximity based on enhanced representation learning using orthogonal mapping. Second, the Region-of-Interest (ROI) is based on spatial griding and metric learning to capture the spatial relationships between POIs to enhance the metric space representation. The final recommendations are based on a multi-head attention mechanism that allocates the weights to different features. The combination of three models, called, LTPM-TRSP approach captures the user-POI, POI-POI, and POI-time relationships by focusing on the informative representation of sequential and spatial data. The category-aware final recommendations based on comprehensive historical behavior and geographical context are quite efficacious. The experimentation on three real-world datasets, Gowalla, Foursquare, and Weeplaces, also suggests the potency compared to other state-of-the-art approaches.
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