社交网络中上下文感知协同过滤算法

围绕上下文感知推荐技术和社会化网络推荐技术的局限性展开研究,提出一种基于社会化网络环境下的名为HCCF的上下文感知协同过滤方法。在充分考虑上下文感知推荐系统实际问题的基础上,首先量化了不同维度的上下文对推荐系统所产生的影响,并在此基础上定义了上下文影响系数。在此基础上引入了社会化网络环境中不同用户之间的相互影响,并采用社会化网络用户信任度进行衡量,最后对上下文因素和社会化网络用户信任度进行综合考虑,提出一种新的相似度计算方法。理论分析和在真实数据集上的实验结果表明,相对于单纯基于上下文的系统过滤算法以及社会化网络推荐方法而言,该算法的准确性和推荐效率均得到一定程度的提升。     

上下文感知推荐系统

近年来,上下文感知推荐系统已成为推荐系统研究领域最为活跃的研究领域之一.如何利用上下文信息进一步提高推荐系统的推荐精确度和用户满意度,成为上下文感知推荐系统的主要任务.从面向过程的角度对最近几年上下文感知推荐系统的研究进展进行综述,对其系统框架、关键技术、主要模型、效用评价以及应用实践等进行了前沿概括、比较和分析.最后,对上下文感知推荐系统有待深入的研究难点和发展趋势进行了展望.     

上下文感知的移动社交网络推荐算法研究

尽管人类活动模式表现出较大的自由度,但也表现出受制于地理和社会限制的结构化模式。针对移动通信网络领域中个性化服务推荐问题,结合社会化网络分析方法,提出一种融合多种上下文信息的社交网络推荐算法。该算法在利用用户的地理位置和时间信息的基础上,深入挖掘潜在的用户社会关系,辅助用户寻找与其偏好相似的用户,然后结合移动用户的社会关系进行相应的推荐,有效解决推荐的准确性问题。这些发现有助于LBSN类系统设计和开发人员更好地了解用户,获知用户的需求,最终完善自己的设计,为用户提供更好的应用服务。在真实数据集上的实验结果验证该算法的可行性和有效性,并且与现有推荐算法相比,具有更高的预测准确度。     

使用贝叶斯网络的社交网络信任驱动推荐方法

为处理推荐行为来源复杂、路径多样、不信任陌生推荐等问题;提出一种在社交网络中信任驱动推荐方法。该方法利用贝叶斯网络;计算用户评分的先验概率分布以及朋友之间的联合条件概率;预测用户在该环境下的评分并将推荐给用户。在信任驱动推荐过程中;预测评分既考虑到用户的偏好;也考虑到用户的社会关系;此外;用户的信息交换只限于朋友之间;能够有效保护用户的隐私。实验结果表明;所提出的推荐方法在预测准确率和推荐覆盖率上具有良好的性能。     

基于多上下文信息的协同过滤推荐算法

随着电子商务和互联网的发展,数据信息呈爆炸式增长,协同过滤算法作为一种简单而高效的推荐算法,能在一定程度上有效地解决信息爆炸问题.但是传统协同过滤算法仅通过单一评分来挖掘相似用户,推荐效果并不占优势.为了提高个性化推荐的质量,如何充分利用用户(物品)的文本、图片、标签等上下文信息以使数据价值最大化是当前推荐系统亟待解决...     

一种基于社会化网络的上下文相关推荐模型

后搜索引擎时代如何构建推荐系统来为用户提供准确的信息成为研究者关注的问题,而有效地利用社会化网络中的上下文信息已然是解决这一问题的一把钥匙。在本文中,我们提出了一种新的推荐模型,通过上下文相关,对社会化网络信息进行再加工,不同于一般上下文信息处理方法,我们通过随机决策树对传统"用户-项目"评价矩阵中评价相似的上下文信息进行区分,然后通过矩阵分解来预测用户的缺失偏好,同时,为了更加充分地利用社会化网络信息,我们将偏好相似用户可能的推荐进行量化,以影响矩阵分解目标函数的结果,以此达到精确推荐的目的,最终以此作为系统推荐。通过实验表明,本文提出的上下文相关推荐模型可获得更高的用户满意度。     

一种基于贝叶斯网络的个性化协同过滤推荐方法研究

针对推荐系统不能有效进行个性化推荐问题,在协同过滤过程中引入语义校验,通过对基于用户的协同过滤推荐结果进行语义校验,剔除概率较低的推荐结果,选择概率较高的结果推荐给用户,从而实现个性化语义推荐。在构建贝叶斯语义校验网络时,增加用户“喜好”偏好字段,通过问卷调查及信息反馈,确定用户对物品的喜好偏好值,确保贝叶斯语义校验网络的科学性。实验结果表明,本方法能剔除用户喜好度较低的物品,提高用户的满意度。     

融合因子分解的上下文推荐算法研究

推荐系统作为帮助用户快速获取有用信息的重要工具之一,得到了深入的研究和广泛的应用.为了更好地挖掘基于上下文信息的推荐场景中\"用户-项目-上下文\"三者之间的潜在关系,定位用户可能的兴趣点,进而提升推荐准确度和用户体验.通过分析目前已有的关于上下文情景数据处理技术的原理和特点,在时间复杂度或者时效性方面仍存在着不足之处,这...     

基于上下文的分布式协同过滤推荐技术

针对传统协同过滤推荐技术应用于大规模动态数据集时难以兼顾准确度和效率的问题,提出一种基于上下文的分布式协同过滤推荐技术,引入推荐上下文的概念,并在此基础上充分考虑用户的即时兴趣以提高推荐的准确度,采用评分矩阵的分布式存储和计算以提高推荐的效率。实验结果表明,该分布式协同过滤技术能同时保证推荐的准确度和效率,使其在大规模动态数据集上的应用更具优势。     

A model of a trust-based recommendation system on a social network

In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents.     

Multiple model based LPV soft sensor development with irregular/missing process output measurement

Data-driven soft sensors have been applied extensively in process industry for process monitoring and control. Linear soft sensors, which are only valid within a relatively small operating envelope, are considered to be insufficient in practice when the processes transit among several operating modes. Moreover, owing to a variety of causes such as malfunction of sensors, multiple rate sampling scheme for different process variables, etc., missing data problem is commonly experienced in process industry. In this paper, soft sensor development with irregular/missing output data is considered and a multiple model based linear parameter varying (LPV) modeling scheme is proposed for handling nonlinearity. The efficiency of the proposed algorithm is demonstrated through several numerical simulation examples as well as a pilot-scale experiment. It is shown through the comparison with the traditional missing data treatment methods in terms of the parameter estimation accuracy that the developed soft sensors enjoy improved performance by employing the expectation-maximization (EM) algorithm in handling the missing process data and model switching problem.     

Context-prediction performance by a dynamic Bayesian network: Emphasis on location prediction in ubiquitous decision support environment

Ubiquitous decision support systems require more intelligent mechanism in which more timely and accurate decision support is available. However, conventional context-aware systems, which have been popular in the ubiquitous decision support systems field, cannot provide such agile and proactive decision support. To fill this research void, this paper proposes a new concept of context prediction mechanism by which the ubiquitous decision support devices are able to predict users’ future contexts in advance, and provide more timely and proactive decision support that users would be satisfied much more. Especially, location prediction is useful because ubiquitous decision support systems could dynamically adapt their decision support contents for a user based on a user’s future location. In this sense, as an alternative for the inference engine mechanism to be used in the ubiquitous decision support systems capable of context-prediction, we propose an inductive approach to recognizing a user’s location by learning a dynamic Bayesian network model. The dynamic Bayesian network model has been evaluated with a set of contextual data from undergraduate students. The evaluation result suggests that a dynamic Bayesian network model offers significant predictive power in the location prediction. Besides, we found that the dynamic Bayesian network model has a great potential for the future types of ubiquitous decision support systems.     

一种探测推荐系统托攻击的无监督算法

托攻击是当前推荐系统面临的重大安全性问题之一. 开发托攻击探测算法已成为保障推荐系统准确性与鲁棒性的关键. 针对现有托攻击探测算法无监督程度较低的局限, 在引入攻击概貌群体效应的定量度量及基于此的遗传优化目标函数的基础上, 将自适应参数的后验推断与攻击探测过程相融合, 提出了迭代贝叶斯推断遗传探测算法, 降低了算法探测性能对系统相关先验知识的依赖. 实验结果显示这种算法能够有效探测各种常见攻击.     

基于信任网络随机游走模型的协同过滤推荐

协同过滤是目前应用最广泛和最成功的推荐技术之一。然而,目前该技术的发展面临着严重的冷启动和稀疏性问题,降低了其推荐质量,因此提出了一种基于信任网络随机游走模型的协同过滤推荐方法。该方法融合了基于信任和项目的协同过滤推荐方法,并引入了信任因子作为引导推荐的重要因素。随机游走模型不仅考虑了信任用户对目标项目的评分,也考虑了他们对与目标项目相似的项目的评分。随着随机游走深度的增加,以相似项目的评分信息来代替目标项目的评分信息的概率也逐渐增大。在Epinions真实数据集上的验证结果表明,该方法在推荐评价指标上比其他算法具有更好的推荐结果。     

Hybrid system of ART and RBF neural networks for online clustering

An online clustering task is considered for machine state monitoring purpose. In the previous authors’ researches a classical ART-2 network was tested for online classification of operational states in the context of a wind turbine monitoring. Some drawbacks, however, were found when a data stream size had been increased. This case is investigated in this paper. Classical ART-2 network can cluster data incorrectly when data points are compared by using Euclidean distance. Furthermore, ART-2 network can lose accuracy when data stream is processed for long time. The way of improving the ART-2 network is considered and two main steps of that are taken. At first, the stereographic projection is implemented. At the second step, a new type of hybrid neural system which consists of ART-2 and RBF networks with data processed by using the stereographic projection is introduced. Tests contained elementary scenarios for low-dimensional cases as well as higher dimensional real data from wind turbine monitoring. All the tests implied that an efficient system for online clustering had been found.     

Progress in context-aware recommender systems — An overview

Recommender Systems are the set of tools and techniques to provide useful recommendations and suggestions to the users to help them in the decision-making process for choosing the right products or services. The recommender systems tailored to leverage contextual information (such as location, time, companion or such) in the recommendation process are called context-aware recommender systems. This paper presents a review on the continual development of context-aware recommender systems by analyzing different kinds of contexts without limiting to any specific application domain. First, an in-depth analysis is conducted on different recommendation algorithms used in context-aware recommender systems. Then this information is used to find out that how these techniques deals with the curse of dimensionality, which is an inherent issue in such systems. Since contexts are primarily based on users’ activity patterns that leads to the development of personalized recommendation services for the users. Thus, this paper also presents a review on how this contextual information is represented (either explicitly or implicitly) in the recommendation process. We also presented a list of datasets and evaluation metrics used in the setting of CARS. We tried to highlight that how algorithmic approaches used in CARS differ from those of conventional RS. In that, we presented what modification or additions are being applied on the top of conventional recommendation approaches to produce context-aware recommendations. Finally, the outstanding challenges and research opportunities are presented in front of the research community for analysis     

基于Web Mining的推荐系统

推荐系统(Recommender System)被电子商务站点用来向顾客提供信息以帮助顾客选择产品,其基本思想是以统计结果或者顾客以前的行为记录为依据,推测顾客未来可能的行为并给出相应的推荐。本文对基于传统技术和Web mining技术的推荐系统进行了简要综述,同时描述了基于Web mining技术的推荐系统的工作流程,重点分析了应用于推荐系统的各种具体Web mining技术及其算法比较。     

Bayesian approach to sensor-based context awareness

The usability of a mobile device and services can be enhanced by context awareness. The aim of this experiment was to expand the set of generally recognizable constituents of context concerning personal mobile device usage. Naive Bayesian networks were applied to classify the contexts of a mobile device user in her normal daily activities. The distinguishing feature of this experiment in comparison to earlier context recognition research is the use of a naive Bayes framework, and an extensive set of audio features derived partly from the algorithms of the upcoming MPEG-7 standard. The classification was based mainly on audio features measured in a home scenario. The classification results indicate that with a resolution of one second in segments of 5–30 seconds, situations can be extracted fairly well, but most of the contexts are likely to be valid only in a restricted scenario. Naive Bayes framework is feasible for context recognition. In real world conditions, the recognition accuracy using leave-one-out cross validation was 87% of true positives and 95% of true negatives, averaged over nine eight-minute scenarios containing 17 segments of different lengths and nine different contexts. Respectively, the reference accuracies measured by testing with training data were 88% and 95%, suggesting that the model was capable of covering the variability introduced in the data on purpose. Reference recognition accuracy in controlled conditions was 96% and 100%, respectively. However, from the applicability viewpoint, generalization remains a problem, as from a wider perspective almost any feature may refer to many possible real world situations.     

利用Tri-training算法解决推荐系统冷启动问题

随着社交网络的发展,推荐系统日趋重要,而冷启动问题是推荐系统中的关键问题。设计了一种基于上下文的半监督学习框架TSEL,对矩阵分解模型SVD进行扩充以支持更多形式的上下文信息,利用Tri-training框架训练各个模型。与其他解决推荐系统冷启动问题的半监督方法(如Co- training)相比,该方法有着更好的效果。Tri-training框架能够更加方便地引入更多推荐模型,具有更好的可扩展性。将Tri-training框架加以 扩展,提出了基于用户活跃度生成无标记教学集合的算法和更加丰富的对矩阵分解模型扩充的形式。在真实数据集MovieLens上进行验证,获得了更好的实验效果。