融合用户感知和多因素的兴趣点推荐

Point-of-interest recommendation integrating user perception and multi-factor

针对传统的基于协同过滤的兴趣点(POI)推荐方法存在数据稀疏问题和现有工作往往单纯利用上下文信息却没有合理平衡各因素的作用影响的问题,提出融合用户感知和多因素的兴趣点推荐模型(UPMF).为基于用户感知的隐式建模提取用户相似性以丰富用户表示,并利用序列、地理和社交等上下文信息构建用户感知协同影响的POI推荐模型,缓解数据稀疏问题.设计新颖的用户感知的融合策略(UPIS),在基于用户感知的同时合理利用各种上下文信息挖掘用户的动态偏好.提出基于分段的活动区域选择算法针对不同活动区域对用户的影响进行建模.实验结果表明,与其他流行的POI推荐方法相比,UPMF在准确率、召回率和归一化折损累计增益(NDCG) 3个评价标准上都有一定程度的提高.在Gowalla和Yelp数据集上,UPMF模型的NDCG@10比SUCP的分别高12.77%、7.24%.

Traditional collaborative filtering-based point-of-interest(POI) recommendation methods suffer from data sparsity and existing work tends to use contextual information alone without a reasonable balance of the effect of each factor. A point-of-interest recommendation model integrating user perception and multi-factor(UPMF) was proposed. User similarities for user perception-based implicit modelling were extracted to enrich user representations, and contextual information such as sequence, geography and social was used to construct POI recommendation models with synergistic user perception influences, which alleviated data sparsity. A novel user perception integration strategy(UPIS) was designed to mine the dynamic preferences of users based on user perceptions while making reasonable use of various contextual information. In addition, a segmentation-based activity area selection algorithm was proposed to model the impact of different activity areas on users. Results show that UPMF can improve the performance on metrics of precision, recall and normalized discounted cumulative gain(NDCG) compared to other popular POI recommendation technologies. Specifically, UPMF outperforms SUCP by12.77% and 7.24% on the Gowalla and Yelp datasets in terms of NDCG@10, respectively.