Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning
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摘要:
针对现有兴趣点推荐的初始化和忽视评论信息语义上下文信息的问题，将深度学习融入推荐系统中已经成为兴趣点推荐研究的热点之一。该文提出一种基于深度学习的混合兴趣点推荐模型(MFM-HNN)。该模型基于神经网络融合评论信息与用户签到信息来提高兴趣点推荐的性能。具体地，利用卷积神经网络学习评论信息的特征表示，利用降噪自动编码对用户签到信息进行初始化。进而，基于扩展的矩阵分解模型融合评论信息特征和用户签到信息的初始值进行兴趣点推荐。在真实签到数据集上进行实验，结果表明所提MFM-HNN模型相比其他先进的兴趣点推荐具有更好的推荐性能。
Abstract:When modeling user preferences, the current researches of recommendation ignore the problem of modeling initialization and the review information accompanied with rating information for recommender models, integrating deep learning into the recommendation system becomes a hotspot of Point-Of-Interest (POI) recommendation. In this paper, a new POI recommendation model called Matrix Factorization Model integrated with Hybrid Neural Networks (MFM-HNN) is proposed. The model improves the performance of POI recommendation by fusing review text and check-in information based on Neural Network (NN). Specifically, the convolutional neural network is used to learn the feature representation of the review text and the check-in information is initialized by using the stacked denoising autoencoder. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the check-in information for POI recommendation. As is shown in the experimental results on real datasets, the proposed MFM-HNN achieves better recommendation performances than the other state-of-the-art POI recommendation algorithms.
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表 1 MFM-HNN模型学习算法
输入：${{{x}}_i},{{{S}}^u},{{{S}}^v},{{\tilde{{S}}}^u},{{\tilde{{S}}}^v},T,B$ 输出：${\cal{L}}$ (1) For $t < $T Do (2) 从兴趣点评论中随机选取一个兴趣点的评论矩阵${{{x}}_i}$进行训练， 训练批次大小为${\beta _0}$，每一个批次的大小为$B$，计算训练过程 中的损失${{\cal{L}}_{\rm{cnn}}}$ (3) if $t > $T or ${{\cal{L}}_{\rm{cnn}}}$足够小 (4) end (5) for $t < $T Do (6) 从兴趣点评分中随机选取一个兴趣点的用户-兴趣点对$({\tilde{{s}}}_i^u,{\tilde{{s}}}_i^v)$ 进行训练，训练批次大小为${\beta _1}$，每一个批次的大小为$B$，计算 训练过程中的损失${{\cal{L}}_{\rm{ui}}}$ (7) if $t > $T or ${{\cal{L}}_{\rm{ui}}}$足够小 (8) end (9) 计算最终的损失值${\cal{L}} = {{\cal{L}}_{\rm{cnn}}} + {{\cal{L}}_{\rm{ui}}}$ (10) return ${\cal{L}}$ 
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表 2 数据集统计
数据统计 LA NYC 用户数量 30,208 47,240 兴趣点数量 142,798 203,765 签到数量(评论) 244,861 388,954 用户-位置矩阵密度 5.68×10–5 4.04×10–5
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