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基于卷积神经网络的旅游推荐模型设计
张佳琳, 柏思佳, 刘 爽
哈尔滨商业大学 计算机与信息工程学院,黑龙江 哈尔滨 150028
摘要:
旅游业和网络时代高速发展,导致旅游信息过载问题日益严重,旅游推荐方法对解决信息过载问题十分重要。传统推荐算法只针对用户和项目之间的评分和基本属性计算相似度进行推荐,但行为需求及具有游客情感因素的评论却被忽视。本文利用卷积神经网络(CNN)对文本评论特征提取进行情感分类,用皮尔逊相似度公式计算相似的用户群体,用平均绝对误差(MAE)对结果误差进行评价。与传统的协同过滤方法进行对比,本文提出的模型能有效降低预测误差。
关键词:  信息过载  旅游推荐  卷积神经网络  协同过滤
DOI:10.11805/TKYDA2019288
分类号:
基金项目:黑龙江省博士后科研启动金资助项目(LBH-Q19028);教育部人文社会科学基金规划项目资助项目(18YJAZH128);黑龙江省哲学社会科学研究规划项目资助项目(18GLB029)
Design of travel recommendation model based on convolutional neural network
ZHANG Jialin, BAI Sijia, LIU Shuang
School of Computer and Information Engineering,Harbin University of Commerce,Harbin Heilongjiang 150028,China
Abstract:
The rapid development of tourism and Internet leads to the increasingly serious problem of tourism information overload. Therefore, tourism recommendation method is very important to solve the problem of information overload. Traditional recommendation algorithms only calculate similarity between users and items based on the score and basic attributes, behavioral needs and comments with tourist emotional factors are ignored. In this paper, Convolutional Neural Network(CNN) is utilized to classify the feature extraction of text comments, Pearson similarity formula is adopted to calculate similar user groups, and Mean Absolute Error(MAE) is employed to evaluate the error of the results. Compared with the traditional collaborative filtering method, the experimental results show that the proposed model can effectively reduce the prediction error.
Key words:  information overload  travel recommendation  convolutional neural network  collaborative filtering

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