基于深度学习的混合兴趣点推荐算法

冯浩; 黄坤; 李晶; 高榕; 刘东华; 宋成芳

doi:10.11999/JEIT180458

基于深度学习的混合兴趣点推荐算法

doi: 10.11999/JEIT180458

冯浩¹,
黄坤¹,
李晶^2, ,,
高榕²,
刘东华²,
宋成芳²

1.
中国舰船研究设计中心武汉 430064
2.
武汉大学计算机学院武汉 430072

基金项目: 国家自然科学基金(41201404)，中央高校基本科研业务费专项资金(2042015gf0009)

详细信息

作者简介:
冯浩：男，1979年生，博士，高级工程师，研究方向为体系结构和系统工程

黄坤：男，1979年生，博士，高级工程师，研究方向为人工智能和系统工程

李晶：男，1967年生，博士，教授，研究方向为数据挖掘和多媒体技术

高榕：男，1981年生，博士，研究方向为数据挖掘和智能推荐

刘东华：女，1989年生，博士生，研究方向为数据挖掘和智能推荐

宋成芳：男，1978年生，博士，讲师，研究方向为可视化分析和位置服务

通讯作者:
李晶　leejingcn@163.com

中图分类号: TP311
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- 被引次数: 0
出版历程
- 收稿日期: 2018-05-14
- 修回日期: 2018-11-26
- 网络出版日期: 2018-12-05
- 刊出日期: 2019-04-01

Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning

1.
China Ship Development and Design Center, Wuhan 430064, China
2.
Computer School, Wuhan University, Wuhan 430072, China

Funds: The National Natural Science Foundation of China (41201404), The Fundamental Research Funds for the Central Universities of China (2042015gf0009)

摘要

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。
- 推荐算法 /
- 兴趣点 /
- 矩阵分解 /
- 神经网络 /
- 深度学习
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.
- Recommendation algorithm /
- Point-Of-Interest (POI) /
- Matrix factorization /
- Neural Network (NN) /
- Deep learning

HTML全文

图 1 基于混合神经网络矩阵分解的兴趣点推荐模型

下载: 全尺寸图片幻灯片

图 2 MFM-HNN模型基于LA数据集和NYC数据集与其他4个模型的推荐性能对比

下载: 全尺寸图片幻灯片

图 3 基于LA和NYC数据集的5个初始化方法的性能对比

下载: 全尺寸图片幻灯片

图 4 MFM-HNN模型基于LA数据集和NYC数据集在不同层数的性能对比

下载: 全尺寸图片幻灯片

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

${\cal{L}}$

下载: 导出CSV

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

下载: 导出CSV

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