基于通信数据的上下文移动用户偏好动态获取方法研究
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摘要
3G网络不仅提高了数据传输速率,还能支持多种媒体形式的数据。云计算的应用,使用户可以通过终端按其所需获取存储资源、计算资源以及相应的软硬件资源。加上移动终端携带比较方便,因此移动用户可以随时随地使用功能简单的终端通过网络完成复杂的任务。另一方面,随着电信网、互联网、普适计算以及物联网技术与应用的飞速发展,移动通信网络在与传统互联网逐渐融合的过程中,对传统互联网的信息服务进行了延伸,为用户提供了比传统通信业务更加丰富多彩的移动网络服务。另一方面,移动终端存在一定的缺陷,例如显示屏幕小、输入输出困难、电源持续使用时间短等。因此,如何从海量信息中及时获取准确的移动用户偏好,为其提供个性化的移动网络服务成为了学术界和工业界近年来的研究热点。
与台式机用户相比,移动用户偏好受上下文影响更加明显。为了准确定位移动用户的偏好,近几年,研究人员将上下文引入到移动用户偏好获取中。虽然上下文的引入可以更准确地定位移动用户偏好,但也给上下文移动用户偏好获取方法带来了一些难题。上下文引入后,原有的用户偏好数量将与上下文实例的种类数成正比例增加,因此学习的响应时间会增长,无法及时准确地满足移动用户的个性化需求。另外,上下文引入后,原有的用户-项目二维矩阵将扩展为用户-项目-上下文三维矩阵,进一步加剧了数据的稀疏性问题,使预测得到的上下文移动用户偏好的精确度降低。针对上述问题,本文根据移动网络的特点,利用移动网络中可以获取到的数据,提出了一种上下文移动用户偏好动态获取的改进方法。本文的研究内容包括：如何根据移动网络中获取的信息计算移动用户之间的信任度、如何对上下文移动用户偏好进行自适应学习、如何缓解协同过滤方法的稀疏性和冷启动问题对移动用户偏好预测结果的影响以及如何在线获取上下文移动用户偏好。在对上述内容研究的基础上，本文取得到了以下儿方面的研究成果。
(1)提出了一种基于通信数据的移动用户信任度计算方法。在移动网络中,隐性获取信任度的方法主要是根据移动用户之间的通信行为进行简单的计算,忽略了上下文信息、用户的社会影响力以及移动用户偏好之间的相似度对信任度的影响,另外没有对信任度的传播距离进行深入研究。针对上述问题,本文提出了一种基于通信数据的移动用户信任度计算方法,在该方法中考虑了上下文移动用户行为、移动用户的社会影响力以及上下文移动用户偏好之间的相似度对信任度的影响。该方法以移动用户之间的通信行为、移动用户之问的相处时间、移动用户使用的移动网络服务以及相应的上下文信息(时间、位置)作为输入数据来获取移动用户之间的信任度。首先根据上下文约束下的移动用户行为以及上下文的权重值来计算移动用户之间的直接信任度；在参考已有文献和六度分割理论的基础上给出了信任度在移动社会网络中的传播距离,并提出了间接信任度的计算方法。然后根据计算得到的信任度构建移动社会化网络,并根据凝聚子群知识对移动社会化网络进行划分,根据划分后的社区结构提出了一种计算移动用户社会影响力的方法。最后计算上下文约束下移动用户偏好之间的相似度,并和前面计算得到的信任度和社会影响力进行融合。
(2)提出了一种上下文移动用户偏好自适应学习方法。移动网络对个性化服务系统的性能提出了更高的要求,但现有研究难以自适应地更新上下文移动用户偏好以为用户提供实时、准确的个性化移动网络服务。针对上述问题,本文提出了一种上下文移动用户偏好自适应学习方法,在保证精确度的情况下缩短了学习的响应时间。该方法通过分析移动网络中上下文约束下的用户行为,检测移动用户偏好是否受上下文影响以及上下文移动用户偏好是否发生变化,并根据上下文实例的权重值和相似度矩阵对上下文进行了量化。当上下文移动用户偏好不发生变化时,只对相应的用户偏好的可信度进行修正；当上下文移动用户偏好发生变化时,采用分类方法进行学习。由于只对部分上下文移动用户偏好进行学习,缩短了学习的响应时间。为了保证上下文移动用户偏好的准确性并进一步加快其学习的响应时间,本文将上下文引入到最小二乘支持向量机分类方法中,提出了一种基于增量一上下文最小二乘支持向量机的移动用户偏好学习方法。
(3)提出了一种基于时间戳的协同过滤方法对用户未使用过的移动网络服务的偏好进行预测。协同过滤方法是预测用户偏好最常用的方法,但传统的协同过滤方法存在稀疏性和冷启动问题,在移动网络中，上下文的引入,进一步加剧了数据的稀疏性问题。本文在已有研究的基础上,提出了一种基于时间戳的协同过滤方法对用户未使用过的移动网络服务的偏好进行预测。首先根据追随时间选出符合要求的上下文移动用户偏好来计算移动用户偏好之间的相似度,并结合移动用户之间的信任度选择近似邻居。然后在预测上下文移动用户偏好之前,根据追随时间和移动用户偏好的可信度选择目标用户最有可能使用的移动网络服务,并根据近似邻居的偏好值预测移动用户对未使用的移动网络服务的偏好。最后为了解决由于新的移动网络服务的推出而造成的冷启动问题,本文通过计算用户对新推出的服务的平均追随时间来判断移动用户是否为时尚型用户,然后利用基于项目的协同过滤方法预测时尚型用户对新的移动网络服务的偏好值。由于本文提出的方法在预测用户偏好时,对上下文移动用户偏好以及移动用户最可能使用的移动网络服务进行了选择,减小了数据的稀疏性,因此,在保证用户偏好精确度的同时,降低了预测的响应时间,更符合移动用户的实时性需求。
(4)提出了一种基于滑动窗口的上下文移动用户偏好在线获取方法。在上下文移动用户偏好获取的现有研究中,大部分方法采用离线方式来获取移动用户的偏好。然而,由于移动网络的实时性特点,需要及时准确地获取移动用户的偏好信息。为了解决上述问题,本文提出了一种基于滑动窗口的上下文移动用户偏好在线获取方法。首先采用基于时间间隔的方式选取合适的滑动窗口和基本窗口。然后在获取上下文移动用户偏好时,根据移动用户使用移动网络服务频率的不同将偏好分为三类：对未使用过的移动网络服务的偏好预测,对这类用户偏好本文使用改进的协同滤波方法进行预测；以前使用过,但最近未使用的移动网络服务的偏好采用遗忘函数进行学习；对经常使用的移动网络服务的偏好采用在线上下文最小二乘支持向量机分类方法进行学习。最后通过实验验证,与离线获取方法相比,在线获取方法可以得到更好的结果,更适合移动网络的需求。
3G network not only enhances the data rate of transmission, but also supports a variety of forms of media data. The application of the cloud computing makes the user can get the storage resources, computing resources, and the corresponding hardware and software resources according to his requirement by the terminal. In addition, the mobile terminal is easier to carry, so the mobile user can finish the complexity operation by the mobile terminal with simple function wherever he is. On the other hand, due to the rapid development of the triple play, pervasive computing and the Internet of Things technology and application, the mobile communication network nutures the information services of the traditional Internet in the process of gradual integration with Internet. It can provide more colorful mobile network services for mobile user than the traditional communications business. However, the mobile terminal has some drawbacks, such as the small interface, difficult input and output, power of short duration and so on. Therefore, in order to satisfy the personalized requirements of mobile user, how to obtain timely the accurate mobile user preference has become the hotspot of the academia and industry in recent years.
Compared with the desktop users, it's more obvious that the mobile user preference is affected by context. In recent years, the researchers add the context to the process of mobile user acquisition to accurately position the mobile user preference. Although the introduction of context can accurately position the mobile user preference, it also brings some challenges to the acquisition method of context mobile user preference. After the introduction of context, the amount of the mobile user preferences will increase in direct proportion to the amount of the context instances. Therefore, the response time of obtaining the mobile user preference will increase and can't satisfy the personalized needs of the mobile user. In addition, the introduction of context makes the user-item matrix extend to user-item-context matrix. This exacerbates the sparsity probems and drops the accuracy of the mobile user preference prediction. In order to obtain the real-time and accurate mobile user preference, we utilize the available data to acquire the mobile user preference according to the characteristics of the mobile network. In the paper, the research includes how to calculate the trust between the mobile users according to the available data in mobile network, how to adaptively learn the context mobile user preference, how to relieve the affect of the sparsity and cold start problem of collaborative filtering to the mobile user preference prediction and how to online acquire the context mobile user preference. Based on the above study, we obtain the following research results.
(1) In the mobile network, the implicit method obtaining the trust mainly performs some simple computations according to the communication behaviors between the mobile users. The existing methods ignore the affect of the context information, the social influence of mobile user as well as the similarity between mobile user preferences to the trust. In addition, these methods rarely make the in-depth study to the propagation distance of the trust. To address the problem, in the paper, a calculation method of the trust based on the telecommunication data is proposed. This proposed method takes into account the affect of the context mobile user behavior, the social influence of mobile user and the similarity between context mobile user preferences to the trust. In the method, the input data includes the communication behaviors and spending time between the mobile users, the mobile network service used by the mobile user as well as the corresponding context information (time, position). Firstly, the direct trust is calculated according the mobile user behavior and obtained the weight of the corresponding context. Based on the methods existed and the theory of the six degrees of separation, a calculation of the propagation distance of the trust is proposed and we can compute the indirect trust according to the propagation distance. Secondly, we can construct the mobile social network according to the obtained trust and employ the cohesion subgroup knowledge to divide the constucted mobile social network. Based on the obtained community structure, a method to calculate the social influence of the mobile user is proposed. Finally, we calculate the similarity between the context mobile user preferences and merge the obtained similarity with the obtained trust and the social influence.
(2) In the mobile network, it has the higher demands for the performance of the personalized mobile network services. However, the research existed is unable to update the context mobile user preference adaptively and provide the real-time, accurate personalized mobile network services for mobile user. In order to resolve the problem, an adaptive learning method of context mobile user preferences is proposed, and the method can ensure the accuracy and reduce the response time. Fristly, through analyzing the context mobile user behaviors, the method judges whether mobile user preference is affected by context or not and detects whether the context mobile user preference change. And then a context quantization method based on the weight and the similarity matrix of context is proposed. If the context mobile user preference does not change, it only needs to modify the confidence of the user preference. If the context mobile preference changes, we employ the classifier to learn the context mobile user preference. Since only part of the context mobile user preferences needs to learn, it reduced the response time of the study. In order to ensure the accuracy of the context mobile user preference and further speed up the response time of the study, the context is introduced into the least square support vector machine classifier. And we employ the increment context least square support vector machine to learn the changed context mobile user preference.
(3) The collaborative filtering is the most common method to predict the mobile user preference. However, the traditional collaborative filtering has the sparsity and cold start problem. In the mobile network, the introduction of the context further exacerbates the sparsity problem. On the basis of previous studies, an improved collaborative filtering method based on the timestamp is proposed to predict the mobile user preference to the unused mobile network service. Firstly, we select the context mobile user preferences that meet the requirements to calculate the similarity between the mobile users according to the follow time. And then we combine the trust with the similarity between the mobile users to select the nearest neighbors. Before predicting the mobile user preference, we select the mobile network services that will most likely be used by the target mobile user according to the follow time and the credibility of mobile user preference. And we predict the mobile user preferences to unused mobile network service according to the preferences of the nearest neighbors. Finally, in order to solve the cold start problem caused by the new mobile network services, we judge whether the mobile user is a fashion user according to the mean value of follow time to the new mobile network services. And the item-based collaborative filtering method is employed to predict the preference of the fashion user to the new network services. Since the proposed method makes the choice to the context mobile user preferences and the most likely used mobile network services when predicting the target mobile user preference, it can alleviate the sparsity of the data. The proposed method can guarantee the accuracy and reduce the response time of the prediction. Therefore, it can better to accord with the real-time requirements of the mobile user.
(4) In the existing auquisition methods of the context mobile user preference, most of them obtain mobile user preference in a static environment. However, due to the real-time characteristics of the mobile network, it is necessary to obtain the mobile user preference accurately and timely. In order to solve the above problem, this paper presents a context mobile user preference online acquisition method based on the sliding window. Firstly, we set the sliding window and the basic window by the time-based method. And then the context mobile user preferences are divided into three categories according the frequency of the mobile network services used:the preference to unused mobile network services, and we use the improved collaborative filtering method to predict; the preference to the mobile network services were used previously, but recently have not been used, and we employ the forgotten function to learn; the preference to the mobile network services that are used frequently, and we use the online context least squares support vector machine to learn. Finally, the experimental results show that the online acqusition method is superior to the offline learning.
与台式机用户相比,移动用户偏好受上下文影响更加明显。为了准确定位移动用户的偏好,近几年,研究人员将上下文引入到移动用户偏好获取中。虽然上下文的引入可以更准确地定位移动用户偏好,但也给上下文移动用户偏好获取方法带来了一些难题。上下文引入后,原有的用户偏好数量将与上下文实例的种类数成正比例增加,因此学习的响应时间会增长,无法及时准确地满足移动用户的个性化需求。另外,上下文引入后,原有的用户-项目二维矩阵将扩展为用户-项目-上下文三维矩阵,进一步加剧了数据的稀疏性问题,使预测得到的上下文移动用户偏好的精确度降低。针对上述问题,本文根据移动网络的特点,利用移动网络中可以获取到的数据,提出了一种上下文移动用户偏好动态获取的改进方法。本文的研究内容包括：如何根据移动网络中获取的信息计算移动用户之间的信任度、如何对上下文移动用户偏好进行自适应学习、如何缓解协同过滤方法的稀疏性和冷启动问题对移动用户偏好预测结果的影响以及如何在线获取上下文移动用户偏好。在对上述内容研究的基础上，本文取得到了以下儿方面的研究成果。
(1)提出了一种基于通信数据的移动用户信任度计算方法。在移动网络中,隐性获取信任度的方法主要是根据移动用户之间的通信行为进行简单的计算,忽略了上下文信息、用户的社会影响力以及移动用户偏好之间的相似度对信任度的影响,另外没有对信任度的传播距离进行深入研究。针对上述问题,本文提出了一种基于通信数据的移动用户信任度计算方法,在该方法中考虑了上下文移动用户行为、移动用户的社会影响力以及上下文移动用户偏好之间的相似度对信任度的影响。该方法以移动用户之间的通信行为、移动用户之问的相处时间、移动用户使用的移动网络服务以及相应的上下文信息(时间、位置)作为输入数据来获取移动用户之间的信任度。首先根据上下文约束下的移动用户行为以及上下文的权重值来计算移动用户之间的直接信任度；在参考已有文献和六度分割理论的基础上给出了信任度在移动社会网络中的传播距离,并提出了间接信任度的计算方法。然后根据计算得到的信任度构建移动社会化网络,并根据凝聚子群知识对移动社会化网络进行划分,根据划分后的社区结构提出了一种计算移动用户社会影响力的方法。最后计算上下文约束下移动用户偏好之间的相似度,并和前面计算得到的信任度和社会影响力进行融合。
(2)提出了一种上下文移动用户偏好自适应学习方法。移动网络对个性化服务系统的性能提出了更高的要求,但现有研究难以自适应地更新上下文移动用户偏好以为用户提供实时、准确的个性化移动网络服务。针对上述问题,本文提出了一种上下文移动用户偏好自适应学习方法,在保证精确度的情况下缩短了学习的响应时间。该方法通过分析移动网络中上下文约束下的用户行为,检测移动用户偏好是否受上下文影响以及上下文移动用户偏好是否发生变化,并根据上下文实例的权重值和相似度矩阵对上下文进行了量化。当上下文移动用户偏好不发生变化时,只对相应的用户偏好的可信度进行修正；当上下文移动用户偏好发生变化时,采用分类方法进行学习。由于只对部分上下文移动用户偏好进行学习,缩短了学习的响应时间。为了保证上下文移动用户偏好的准确性并进一步加快其学习的响应时间,本文将上下文引入到最小二乘支持向量机分类方法中,提出了一种基于增量一上下文最小二乘支持向量机的移动用户偏好学习方法。
(3)提出了一种基于时间戳的协同过滤方法对用户未使用过的移动网络服务的偏好进行预测。协同过滤方法是预测用户偏好最常用的方法,但传统的协同过滤方法存在稀疏性和冷启动问题,在移动网络中，上下文的引入,进一步加剧了数据的稀疏性问题。本文在已有研究的基础上,提出了一种基于时间戳的协同过滤方法对用户未使用过的移动网络服务的偏好进行预测。首先根据追随时间选出符合要求的上下文移动用户偏好来计算移动用户偏好之间的相似度,并结合移动用户之间的信任度选择近似邻居。然后在预测上下文移动用户偏好之前,根据追随时间和移动用户偏好的可信度选择目标用户最有可能使用的移动网络服务,并根据近似邻居的偏好值预测移动用户对未使用的移动网络服务的偏好。最后为了解决由于新的移动网络服务的推出而造成的冷启动问题,本文通过计算用户对新推出的服务的平均追随时间来判断移动用户是否为时尚型用户,然后利用基于项目的协同过滤方法预测时尚型用户对新的移动网络服务的偏好值。由于本文提出的方法在预测用户偏好时,对上下文移动用户偏好以及移动用户最可能使用的移动网络服务进行了选择,减小了数据的稀疏性,因此,在保证用户偏好精确度的同时,降低了预测的响应时间,更符合移动用户的实时性需求。
(4)提出了一种基于滑动窗口的上下文移动用户偏好在线获取方法。在上下文移动用户偏好获取的现有研究中,大部分方法采用离线方式来获取移动用户的偏好。然而,由于移动网络的实时性特点,需要及时准确地获取移动用户的偏好信息。为了解决上述问题,本文提出了一种基于滑动窗口的上下文移动用户偏好在线获取方法。首先采用基于时间间隔的方式选取合适的滑动窗口和基本窗口。然后在获取上下文移动用户偏好时,根据移动用户使用移动网络服务频率的不同将偏好分为三类：对未使用过的移动网络服务的偏好预测,对这类用户偏好本文使用改进的协同滤波方法进行预测；以前使用过,但最近未使用的移动网络服务的偏好采用遗忘函数进行学习；对经常使用的移动网络服务的偏好采用在线上下文最小二乘支持向量机分类方法进行学习。最后通过实验验证,与离线获取方法相比,在线获取方法可以得到更好的结果,更适合移动网络的需求。
3G network not only enhances the data rate of transmission, but also supports a variety of forms of media data. The application of the cloud computing makes the user can get the storage resources, computing resources, and the corresponding hardware and software resources according to his requirement by the terminal. In addition, the mobile terminal is easier to carry, so the mobile user can finish the complexity operation by the mobile terminal with simple function wherever he is. On the other hand, due to the rapid development of the triple play, pervasive computing and the Internet of Things technology and application, the mobile communication network nutures the information services of the traditional Internet in the process of gradual integration with Internet. It can provide more colorful mobile network services for mobile user than the traditional communications business. However, the mobile terminal has some drawbacks, such as the small interface, difficult input and output, power of short duration and so on. Therefore, in order to satisfy the personalized requirements of mobile user, how to obtain timely the accurate mobile user preference has become the hotspot of the academia and industry in recent years.
Compared with the desktop users, it's more obvious that the mobile user preference is affected by context. In recent years, the researchers add the context to the process of mobile user acquisition to accurately position the mobile user preference. Although the introduction of context can accurately position the mobile user preference, it also brings some challenges to the acquisition method of context mobile user preference. After the introduction of context, the amount of the mobile user preferences will increase in direct proportion to the amount of the context instances. Therefore, the response time of obtaining the mobile user preference will increase and can't satisfy the personalized needs of the mobile user. In addition, the introduction of context makes the user-item matrix extend to user-item-context matrix. This exacerbates the sparsity probems and drops the accuracy of the mobile user preference prediction. In order to obtain the real-time and accurate mobile user preference, we utilize the available data to acquire the mobile user preference according to the characteristics of the mobile network. In the paper, the research includes how to calculate the trust between the mobile users according to the available data in mobile network, how to adaptively learn the context mobile user preference, how to relieve the affect of the sparsity and cold start problem of collaborative filtering to the mobile user preference prediction and how to online acquire the context mobile user preference. Based on the above study, we obtain the following research results.
(1) In the mobile network, the implicit method obtaining the trust mainly performs some simple computations according to the communication behaviors between the mobile users. The existing methods ignore the affect of the context information, the social influence of mobile user as well as the similarity between mobile user preferences to the trust. In addition, these methods rarely make the in-depth study to the propagation distance of the trust. To address the problem, in the paper, a calculation method of the trust based on the telecommunication data is proposed. This proposed method takes into account the affect of the context mobile user behavior, the social influence of mobile user and the similarity between context mobile user preferences to the trust. In the method, the input data includes the communication behaviors and spending time between the mobile users, the mobile network service used by the mobile user as well as the corresponding context information (time, position). Firstly, the direct trust is calculated according the mobile user behavior and obtained the weight of the corresponding context. Based on the methods existed and the theory of the six degrees of separation, a calculation of the propagation distance of the trust is proposed and we can compute the indirect trust according to the propagation distance. Secondly, we can construct the mobile social network according to the obtained trust and employ the cohesion subgroup knowledge to divide the constucted mobile social network. Based on the obtained community structure, a method to calculate the social influence of the mobile user is proposed. Finally, we calculate the similarity between the context mobile user preferences and merge the obtained similarity with the obtained trust and the social influence.
(2) In the mobile network, it has the higher demands for the performance of the personalized mobile network services. However, the research existed is unable to update the context mobile user preference adaptively and provide the real-time, accurate personalized mobile network services for mobile user. In order to resolve the problem, an adaptive learning method of context mobile user preferences is proposed, and the method can ensure the accuracy and reduce the response time. Fristly, through analyzing the context mobile user behaviors, the method judges whether mobile user preference is affected by context or not and detects whether the context mobile user preference change. And then a context quantization method based on the weight and the similarity matrix of context is proposed. If the context mobile user preference does not change, it only needs to modify the confidence of the user preference. If the context mobile preference changes, we employ the classifier to learn the context mobile user preference. Since only part of the context mobile user preferences needs to learn, it reduced the response time of the study. In order to ensure the accuracy of the context mobile user preference and further speed up the response time of the study, the context is introduced into the least square support vector machine classifier. And we employ the increment context least square support vector machine to learn the changed context mobile user preference.
(3) The collaborative filtering is the most common method to predict the mobile user preference. However, the traditional collaborative filtering has the sparsity and cold start problem. In the mobile network, the introduction of the context further exacerbates the sparsity problem. On the basis of previous studies, an improved collaborative filtering method based on the timestamp is proposed to predict the mobile user preference to the unused mobile network service. Firstly, we select the context mobile user preferences that meet the requirements to calculate the similarity between the mobile users according to the follow time. And then we combine the trust with the similarity between the mobile users to select the nearest neighbors. Before predicting the mobile user preference, we select the mobile network services that will most likely be used by the target mobile user according to the follow time and the credibility of mobile user preference. And we predict the mobile user preferences to unused mobile network service according to the preferences of the nearest neighbors. Finally, in order to solve the cold start problem caused by the new mobile network services, we judge whether the mobile user is a fashion user according to the mean value of follow time to the new mobile network services. And the item-based collaborative filtering method is employed to predict the preference of the fashion user to the new network services. Since the proposed method makes the choice to the context mobile user preferences and the most likely used mobile network services when predicting the target mobile user preference, it can alleviate the sparsity of the data. The proposed method can guarantee the accuracy and reduce the response time of the prediction. Therefore, it can better to accord with the real-time requirements of the mobile user.
(4) In the existing auquisition methods of the context mobile user preference, most of them obtain mobile user preference in a static environment. However, due to the real-time characteristics of the mobile network, it is necessary to obtain the mobile user preference accurately and timely. In order to solve the above problem, this paper presents a context mobile user preference online acquisition method based on the sliding window. Firstly, we set the sliding window and the basic window by the time-based method. And then the context mobile user preferences are divided into three categories according the frequency of the mobile network services used:the preference to unused mobile network services, and we use the improved collaborative filtering method to predict; the preference to the mobile network services were used previously, but recently have not been used, and we employ the forgotten function to learn; the preference to the mobile network services that are used frequently, and we use the online context least squares support vector machine to learn. Finally, the experimental results show that the online acqusition method is superior to the offline learning.
引文
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