【作者】 刘继；

【导师】 邓贵仕；

【作者基本信息】 大连理工大学 ， 管理科学与工程， 2010， 博士

【摘要】 随着各种网络的快速发展,不断增长的信息远远超出人们的处理能力。人们常常会感觉到自己淹没在大量的信息中而无法有效地找到所需要的信息,“信息过载”问题日趋严重。为了给人们提供满意的信息和服务,推荐技术应运而生,成为目前众多学者和网络用户关心的核心技术。各种网络社区和社会网络服务的发展使得推荐技术已经不能以用户和各种资源对象的简单关系来描述,用户对资源的需求也不能单一地以个性化推荐来解释,而应该考虑用户个性和共性的辩证统一。本文提出利用网络协作来构建整个推荐体系,使人与外部世界更好地在网络协作的基础上进行信息交互。针对协作推荐中的关键问题(稀疏性,冷启动、可扩展性、兴趣变化、认知反馈等),本文基于网络社团分析对网络协作化的推荐方法进行了以下方面的研究：1从用户与资源对象的网络关系出发,构建了推荐系统的网络体系构架。该推荐网络体系由用户-资源对象二分网络、资源对象关联网络和用户网络构成,分为三个层次：(1)用户-资源对象网络：用户和资源对象构成的关系网络。(2)资源对象关联网络：资源对象之间构成的关系网络。(3)用户社会网络：外部用户的社会网络。通过该三层网络体系可以更好地刻画整个推荐系统。目前常见的多种推荐方法成为该体系的一种特例,另外该体系纳入了社会网络,使推荐更接近当前互联网络发展的现实情况。更重要的是,网络社团构成体系的特殊单位,成为研究和分析推荐问题的理论与现实基础之一。2.对推荐系统的网络社团进行了分析,讨论了基于网络社团分析的协作推荐研究的理论和现实的意义,并提出了基于代表性能量的网络社团发现方法。网络社团的现实性与网络世界的网络社团效应的统一,使得分析网络社团可以将推荐问题的精度、效率和社会化统一起来。本文在对网络社团发现的基础上,对利用“模块度”进行社团划分的局限性进行了一定的论述,提出了基于代表能量竞争的网络社团发现方法。该方法不需要模块度的优化,而是利用网络节点之间不同的亲疏关系使得社团的代表在竞争中出现,不但可以得到社团的划分个数而且可直接获得每个社团内的结构概貌。由于网络社团内的成员的连接边数比社团间的连接边数多,社团内成员获得本社团成员的代表能量较高。社团代表通过代表竞争过程获得,代表的个数即为社团的个数。当社团的亲疏关系反映整个网络的社团化不明显时,竞争胜出的代表也会减少。所以,与目前基于网络分割的社团发现方法相比,本文提出的代表能量竞争的方法更符合社会、自然的团落发展的规律。3.提出了基于网络社团发现的协作过滤推荐方法。该方法将网络社团划分与团内协作推荐结合起来,使得推荐在团内依据近邻用户的偏好相似进行一致性逼近。首先利用代表能量竞争的社团发现方法对用户-资源对象的共同评价关系网络进行初步的社团发现,接着对团内评价进行均一化处理,利用偏好相似进行协作推荐。另外按照最近邻用户和最近邻项目来发现离目标预测链接的最近邻网络社团,将该社团映射到最近邻矩阵上。利用基于项目的协作过滤算法对矩阵中的空数据域进行预测填补。鉴于不同项目对目标预测的贡献不同,利用项目相关性在用户邻居候选集中对目标用户的邻居进行精选,实现了对目标评价的超线性预测。4.提出了基于时间加权的网络推荐方法。该方法利用用户-资源对象的选择(评价)时间对动态资源分配网络的边进行加权,对推荐系统进行动态网络模拟。详细分析了推荐系统中用户对资源对象的兴趣随时间衰减的影响,利用时间衰减因子对带有时间加权的二分资源动态网络进行兴趣衰减分析,最近用户的选择行为对资源对象间的推荐能量的流动权将获得较高的贡献度,充分体现用户兴趣的时间效应特性。而在处理用户在资源对象间的兴趣转移时候,利用时间延迟因子模拟资源之间在单个用户流动的通畅性,时间延迟因子小的资源对象间获得较高的流动权。在对推荐能量进一步分析的基础上,发现资源网络的推荐过程中有一定的冗余能量转移,通过转移节点的网络社团交叠程度来消减推荐过程中冗余能量的转移。基于时间加权的资源分配网络使推荐系统具有网络化的时间动态推荐预测能力。5.提出了基于网络云团的人机协作聚合推荐方法,构建了贝叶斯反馈云模型。本文在云计算分析的基础上,提出了基于网络云团的协作聚合推荐方法。在网络云团内的人与系统反馈认知中将人类认知的先验性和云模型的特点结合起来,构建了贝叶斯反馈云模型。对不确定性推荐反馈概念的定量和定性测度在人机之间相互转化进行了分析,对云滴校验进行了详细的设计,给出了贝叶斯反馈云的统计描述。利用该模型得到云团内的推荐偏好云,基于协作实体进行云团聚合协作推荐。在项目云团基础上,对推荐中的新用户冷启动问题进行了应用分析。更多还原

【Abstract】 With the development of networks, the amount of information increases beyond our ability to process it. The clients may feel that they are submerging in the information sea and difficult to find what they need. The information overload is getting worse and worse. Recommendation technology attracts more attentions of scholars and internet users in that it is expected to help clients efficiently find what they need online.The development of recent social network services (SNS) and network communities hinders recommendation technology to work well under simple relationships between various resource objects and users. The user’s requirement for resources cannot be simply fulfilled by personal recommendation. Better recommendation can be regarded as a dialectic integration of users’personalities and their overall characters. This paper builds the entire recommendation system with network collaboration, which will affect the information exchange between people and others. As for many problems in collaborative recommendation: sparseness, cold start, scalability, interest change, cognitive feedback and other issues, this article presents some methods to solve those problems based on the theory of network community. The research works are listed as follows:1. The network architecture of recommendation system on expanding user-object network is presented. The recommendation network system is divided into three layers:user-resource object bipartite network, resource object network and user social network. (1) User-resource object network:users and resource objects constitute the network of relationships. (2) Resource object network:resource object network can be established with the relationships of resource objects. (3) User social network:the online relationships among different users.The three-tier network better describes the recommendation system because some recommendation methods can be looked as special cases of the system. Significantly, the network community constitutes special unit of recommendation system, being regarded as theoretical and practical base for solving many recommendation problems.2. The network communities of recommendation system are analyzed and theoretical and practical significance of network community-based collaborative recommendation are discussed in detail. A new network community detecting algorithm based on representative energy is presented. The integration of reality and the theory of network community i.e. socialization of recommendation system makes the study of issues be more accurate and efficient. In this paper, the limited resolution of network community discovery based on the use of "modularity" is described in detail. A new network community discovery method based on competing representative energy is proposed. This method does not require modularity optimization; rather it depends on the behavior of competing for community representatives. The method can divides network into those communities, it can also draw the skeleton of each community directly. Since the network connections within community are denser than those between communities, the members can obtain higher representative energy from those nodes in same community than from nodes in other communities. Community representatives emerge through competitive process naturally, and the number of representatives is the number of communities. When community affinities of entire network community are not obvious, the number of representatives who win out in the competition will decrease. Therefore, compared with other methods of network division, this method based on energy competition can show the natural law of evolving groups.3.In this work, a network community-based collaborative filtering recommendation method is proposed. The method is the combination of two tasks i.e. division of the network and collaboration recommendation within community, which consistently predicts the preference of user depending on his nearest neighbors. First of all, representative energy analysis is employed to discover the community in co-rating network. Then, the community ratings are normalized so that collaborative recommendation can implement with the similarity of preferences between users. The nearest user neighbors and the nearest object neighbors constitute the nearest community which is the nearest space to target prediction, and the community can be projected into the nearest neighbor matrix. Moreover, some empty data in the matrix can be filled with values which are predicted with object-based collaborative filtering algorithm. User’s nearest neighbors are selected from candidates, which contributes to predict different objects. The nearest community-based collaborative filtering can approximate the target forecast in a super-linear way.4. A time-weighted network recommendation method is presented. This method weighs the edge of resource allocation network with time and models the dynamic network of recommendation system, and analyses the influence of interest varied with time in user-object network, which describes the decay of interest through time attenuation. The latter selecting can make bigger contribution to the energy of recommendation. As the interest diversion is concerned, the paper presents diversion delay factor to model the recommendation flow among different resource objects. Redundant energy transition is discovered by analyzing recommendation energy; the paper tries to eliminate the redundant energy with overlapping degree of transfer node. The recommendation method based time-weighted network enable the recommendation system with structural dynamic prediction ability.5. This paper proposes a method of human-machine collaborative aggregating recommendation based on network cloud community, and constructs a Bayesian feedback cloud model. Under the analysis of cloud computing, an aggregating recommendation method based on network cloud community is presented. A Bayesian feedback cloud model is constructed with the combination of human being’s apriority and feature of cloud model. Based on analysis of transformation between quantitative and qualitative measurement of uncertain concepts, the paper designs cloud drop tester in detail, and presents statistical description of Bayesian feedback cloud. Preferences cloud can be obtained by using the model within the clouds community, and the prediction for recommendation can be aggregated on cloud community with preference. The method based on object cloud community has been applied in alleviating new user cold-start problem in recommendation.更多还原