基于社交媒体的推荐技术若干问题研究
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摘要
随着Web2.0以及社会化媒体的发展,尤其是近5年来Facebook、Blogger和Twitter等网络应用的盛行,不仅创造了“全民记者时代”,更是带来了社交媒体领域的信息泛滥。面对这些近乎灾难的数据,一个很自然的问题是：用户如何才能找到有用的信息呢?个性化推荐技术作为一种解决“信息过载”的有效手段,毫无疑问成为了首选。但是社会化媒体中用户人数、信息数据的爆炸增长以及用户结构的不断复杂化,使得推荐系统不得不面临一些新的挑战,尤其是数据的极度稀疏、实时推荐和可信任推荐三个问题在社交媒体推荐中更为突出。
围绕着如何克服数据稀疏问题、加快推荐的速度、提高推荐的可信度和保证推荐的准确度,本文对社交媒体环境下推荐系统中涉及的若干问题进行了有益的探索和研究。主要的研究工作和创新点如下：
(1)在对用户行为数据进行统计学分析的基础上,提出了基于用户行为的协同过滤推荐方法。该方法通过分析用户行为的数据,发现用户行为的一些全局结构和隐含特征,并将这些信息与用户行为数据一起作为协同过滤推荐方法的依据。实验表明,该方法在一定程度上提高了推荐的准确度。
(2)针对用户行为数据的稀疏性问题提出了基于语义的矩阵分解预测方法。该方法通过提取用户行为中的一些语义信息,如隐含特征信息、上下文时间信息、位置信息等,并采用矩阵分解的方法来补全用户行为矩阵中的缺失数据。最后,根据已补全的用户行为矩阵信息为用户进行推荐预测。
(3)针对实时推荐问题提出了基于Co-clustering的聚类推荐方法。该方法首先采用Co-clustering聚类方法来对用户和行为进行离线聚类；然后基于离线聚类的结果,结合用户的最近行为实现在线的实时推荐；最后,通过增量更新模型不间断地更新用户行为数据来保证离线聚类结果的准确性。该方法,一方面通过聚类法减少最近邻用户的搜索空间来降低计算复杂度；另一方面通过将离线聚类和在线实时推荐分开来减少在线推荐的计算时间。
(4)提出了利用社会媒体中的社会关系来提高推荐可信度的方法。该方法结合现实生活中的社会关系,引入社会网络中的个人信誉度和用户之间的信任度指数来对原有推荐系统中的相似度模型进行补充,实现对用户的可信任推荐。实验表明,该方法一方面能保证较好的推荐准确度,另一方面能在一定程度内保证系统的推荐效果不受外来因素的干扰和破坏。
With the development of Web2.0and social media, especially the last five years, the popularity of Facebook, Blogger and Twitter, and other network applications, not only created the era of the "National Correspondent", but also brought the information in the field of social media flooding. Faced with the nearly-disaster data, a natural question is:how users can find useful information? Personalized recommendation technology, as an effective solution to "information overload", no doubt, becomes the preferred. A large number of users in social media, the explosive growth of information and data, and the complicacy of user structure, make the recommender system had to face some new challenges, especially the extremely sparse data, real-time recommendation and trusted recommendation, which are three key issues in social media recommended.
Around how to overcome the data sparseness problem, speed up the speed of recommendation, improve the reliability of the recommendation and to ensure the accuracy of the recommendation, number of issues involved in the recommender system in the social media environment, are explored and researched in this dissertation. The main research and innovation are as follows:
(1) Based on the statistical analysis of user behavior data, collaborative filtering algorithm based on user behavior is presented. Through the analysis of user behavior data, this method can find the global structure and hidden features of the user behaviors, and combine these information and user behavior data as the basis for collaborative filtering recommendation method. The experiments show that the method, to some extent, improves the accuracy of the recommended.
(2) For the scarcity data of the user behaviors, semantic-based matrix decomposition prediction methods are presented. The methods extract some semantic information of user behaviors, such as the implicit characteristics of information, context and time information, location information, and use the matrix factorization method to fill in missing data in the matrix of the whole user behavior. Then, the recommended prediction is based on the complement matrix information of user behaviors.
(3) For the real-time recommendation, the recommended method based on the clustering of Co-clustering is proposed. First, the method uses Co-clustering method for offline clustering of users and behaviors; And then realizes online real-time recommendation based on the results of the off-line clustering, combining with the recent behavior of the user's; Finally, the incremental update model continuously updates the user behavior data to ensure the accuracy of the offline clustering results. The method, on the one hand, is to reduce the computational complexity by clustering to reduce the search space of nearest neighbor users; On the other hand, is to reduce the computation time of online real-time recommendation by separating online recommended and the off-line clustering.
(4) By using the social relationships in social media, the method is proposed to improve the credibility of recommended. This method combines the real-life social relationships, personal credibility factors in the social network and trust factors between the users, to supplement the similarity model in the original recommendation system, and to achieve the user's trusted recommendation. The experiments show that the method, on the one hand, can ensure better recommendation accuracy, on the other hand, can effectively prevent the interference of the external factors to a certain extent.
围绕着如何克服数据稀疏问题、加快推荐的速度、提高推荐的可信度和保证推荐的准确度,本文对社交媒体环境下推荐系统中涉及的若干问题进行了有益的探索和研究。主要的研究工作和创新点如下：
(1)在对用户行为数据进行统计学分析的基础上,提出了基于用户行为的协同过滤推荐方法。该方法通过分析用户行为的数据,发现用户行为的一些全局结构和隐含特征,并将这些信息与用户行为数据一起作为协同过滤推荐方法的依据。实验表明,该方法在一定程度上提高了推荐的准确度。
(2)针对用户行为数据的稀疏性问题提出了基于语义的矩阵分解预测方法。该方法通过提取用户行为中的一些语义信息,如隐含特征信息、上下文时间信息、位置信息等,并采用矩阵分解的方法来补全用户行为矩阵中的缺失数据。最后,根据已补全的用户行为矩阵信息为用户进行推荐预测。
(3)针对实时推荐问题提出了基于Co-clustering的聚类推荐方法。该方法首先采用Co-clustering聚类方法来对用户和行为进行离线聚类；然后基于离线聚类的结果,结合用户的最近行为实现在线的实时推荐；最后,通过增量更新模型不间断地更新用户行为数据来保证离线聚类结果的准确性。该方法,一方面通过聚类法减少最近邻用户的搜索空间来降低计算复杂度；另一方面通过将离线聚类和在线实时推荐分开来减少在线推荐的计算时间。
(4)提出了利用社会媒体中的社会关系来提高推荐可信度的方法。该方法结合现实生活中的社会关系,引入社会网络中的个人信誉度和用户之间的信任度指数来对原有推荐系统中的相似度模型进行补充,实现对用户的可信任推荐。实验表明,该方法一方面能保证较好的推荐准确度,另一方面能在一定程度内保证系统的推荐效果不受外来因素的干扰和破坏。
With the development of Web2.0and social media, especially the last five years, the popularity of Facebook, Blogger and Twitter, and other network applications, not only created the era of the "National Correspondent", but also brought the information in the field of social media flooding. Faced with the nearly-disaster data, a natural question is:how users can find useful information? Personalized recommendation technology, as an effective solution to "information overload", no doubt, becomes the preferred. A large number of users in social media, the explosive growth of information and data, and the complicacy of user structure, make the recommender system had to face some new challenges, especially the extremely sparse data, real-time recommendation and trusted recommendation, which are three key issues in social media recommended.
Around how to overcome the data sparseness problem, speed up the speed of recommendation, improve the reliability of the recommendation and to ensure the accuracy of the recommendation, number of issues involved in the recommender system in the social media environment, are explored and researched in this dissertation. The main research and innovation are as follows:
(1) Based on the statistical analysis of user behavior data, collaborative filtering algorithm based on user behavior is presented. Through the analysis of user behavior data, this method can find the global structure and hidden features of the user behaviors, and combine these information and user behavior data as the basis for collaborative filtering recommendation method. The experiments show that the method, to some extent, improves the accuracy of the recommended.
(2) For the scarcity data of the user behaviors, semantic-based matrix decomposition prediction methods are presented. The methods extract some semantic information of user behaviors, such as the implicit characteristics of information, context and time information, location information, and use the matrix factorization method to fill in missing data in the matrix of the whole user behavior. Then, the recommended prediction is based on the complement matrix information of user behaviors.
(3) For the real-time recommendation, the recommended method based on the clustering of Co-clustering is proposed. First, the method uses Co-clustering method for offline clustering of users and behaviors; And then realizes online real-time recommendation based on the results of the off-line clustering, combining with the recent behavior of the user's; Finally, the incremental update model continuously updates the user behavior data to ensure the accuracy of the offline clustering results. The method, on the one hand, is to reduce the computational complexity by clustering to reduce the search space of nearest neighbor users; On the other hand, is to reduce the computation time of online real-time recommendation by separating online recommended and the off-line clustering.
(4) By using the social relationships in social media, the method is proposed to improve the credibility of recommended. This method combines the real-life social relationships, personal credibility factors in the social network and trust factors between the users, to supplement the similarity model in the original recommendation system, and to achieve the user's trusted recommendation. The experiments show that the method, on the one hand, can ensure better recommendation accuracy, on the other hand, can effectively prevent the interference of the external factors to a certain extent.
引文
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