摘要
移动互联网爆炸式增长、电子商务的迅猛发展以及智能手机的快速普及，使“移动互联新生态”在全球范围内迅速成长，其个性化推荐系统也跻身为提高企业竞争力、满足用户即时个性化需求的利器。但移动商务的特殊性使传统推荐系统难以简单移植并满足“数字宇宙”时代的特殊需求。本文的研究目的在于通过系统结合用户情景兴趣与云计算技术，提出面向云环境用户情景兴趣的移动商务推荐模型，解决云环境下移动商务用户的情景推荐、信任推荐、多兴趣推荐以及服务质量偏好预测推荐等问题，最终为移动商务用户推荐与当前情景最为关联的即时服务。针对论文特点，在研究过程中使用了社交网络方法、蚁群聚类方法、蚁群神经网络方法等。
本文从事的研究工作主要如下：
(1)针对移动商务情景推荐问题，结合云计算技术与移动商务用户情景，构建了基于移动商务用户情景兴趣的协同过滤推荐模型。通过计算基于移动商务用户的情景相似度，构造了与目标用户当前情景相似的情景集合，然后建立基于项目评分情景和评分矩阵，并通过MapReduce化的协同过滤推荐方法实现并行推荐。
(2)针对传统协同过滤存在的数据稀疏、冷启动问题，构建了不同信任信息条件下云环境用户情景兴趣的推荐模型。基于情景兴趣与富信任信息的移动商务推荐模型引入了用户间信任关系解决协同过滤算法存在的数据稀疏性问题，并采用MapReduce的数据处理方式解决大规模的复杂社会网推荐问题；基于情景兴趣与稀疏信任信息的移动商务推荐模型主要致力于解决现实情况下可用信任信息较少导致的推荐不准确问题。
(3)针对用户单兴趣表示存在的问题，构建了云环境用户多情景兴趣的移动商务蚁群聚类推荐模型。该模型通过对用户情景兴趣进行层次划分，使用改进的层次聚类算法和新的目标函数生成聚类的兴趣树，构造多层次蚁群搜索路径来发现目标用户的若干最近邻类簇，利用类簇内其他用户对目标项目的评分预测未评分项目的评分，最后结合MapReduce与协同过滤思想设计相应推荐算法。
(4)针对移动商务服务质量(QoS)偏好预测推荐问题，构建了基于用户位置情景与蚁群神经网络的QoS服务预测混合推荐模型。模型基于用户位置情景信息将网站的所有服务与用户按自治系统进行聚类，由此构建用户——服务矩阵；然后将采用基于用户和基于项目的方法预测的QoS值合并为一个矩阵，以此作为MapReduce化的蚁群神经网络输入进行权值训练，通过权值训练可获得不同协同过滤方法在不同环境下对应的权值；最后根据这些权值得到最终服务质量预测的QoS值。
(5)针对用户情景兴趣的移动商务推荐模型应用问题，设计了一个移动商务景点推荐架构并进行了实证研究。首先，基于云计算思想建立旅游移动商务的景点推荐系统原型框架，在此基础上构建面向景点推荐的用户情景兴趣模型；然后，以秦皇岛高校大学生为研究对象进行实证研究。实证结果验证了本文景点推荐系统的可行性，能够满足手机用户当前的个性化景点推荐需求。
With the explosive growth of mobile Internet, rapid development of electronicbusiness and rapid popularization of intelligent mobile phone, a mobile Internet newecology is emerging globally. But the particularity of mobile commerce make thetraditional recommendation system diffcult to meet the special needs of this digitaluniverse era. The purpose of this study is to combination the user situational interest andcloud computing technology to propose a cloud-oriented users situational interestrecommendation model, thus solving the trust recommendation, multi interestrecommendation and service of quality recommendation. According to the characteristicsof this paper, we use collaborative filtering method, social network method, ant colonyclustering method and ant colony neural network method.
The contents in this paper are listed as follows.
Firstly, aiming to resolve the mobile commerce scenario suggested problems, cloudcomputing technology and mobile user context are combined to propose a collaborativefiltering model based on user interest in mobile scenarios. Through computing the scenesimilarity based on mobile users, we find similar scenarios constructed target user setcurrent situation, and then establish the project scoring scene and scoring matrix. Basedon MapReduce, we propose a collaborative filtering recommendation method to realizeparallel recommendation.
Secondly, to address the traditional collaborative filtering of data sparsity and coldstart problem, a cloud user situational interest recommendation model under differenttrust information environment is proposed. A mobile commerce situational interest andrich trust information recommendation model trust relationship is introduced to solve thedata sparseness problem existing in collaborative filtering algorithm, and processingmethods to solve complex social network recommended by MapReduce data is proposed.Mobile commerce recommendation model of situational interest and sparse trustinformation is mainly devoted to solve trust less information available the realitycircumstances lead to inaccurate problem based on the recommendations, specifically the situational interest similarity matrix and potential trust degree matrix are combined into acomposite matrix. Then, we use the Resnick recommended formula and MapReduce dataprocessing method to implement the recommender model.
Thirdly, to address the user single interest problems, a representation model is givenbased on mobile user multi interest level. At the same time, ant colony algorithm andhierarchical clustering method is used in the mobile commerce ant colony clusteringprocess. The level of user situational interest is used in the target function to generateclustering hierarchical clustering algorithm and the new system tree, and constructmulti-level ant colony search path is found according to some target user's nearestneighbor cluster. Then, we use other users within the cluster on the target score to predictproject the not-scored items. Finally, the combination of MapReduce and collaborativefiltering recommendation algorithm is designed in the experiment.
Fourthly, to resolve the service quality of mobile commerce preference predictiverecommendation problem, a hybrid recommendation model is presented for theprediction of user location scenarios. Firstly, based on the user context information tocluster all location service and users of the site by the autonomous system, we build auser-service matrix; and then, the user and the forecast of project method based onpredictive value is given to train the weights of Ant Colony Neural Network based onMapReduce. Finally, according to these weights, the system gives the final servicequality prediction value.
Finally, to implement the model in real world, a mobile commerce siterecommendation framework is designed in the empirical research. First of all, weestablish a mobile commerce tourism attractions recommendation system framework. Onthe basis of constructing the model of user oriented situational interest, we make anempirical study of students in Qinhuangdao university. Empirical results indicates thefeasibility of this scenic spot recommendation system to meet the personalized needs ofmobile users.
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