Collaborative recommendation algorithm based on probabilistic matrix factorization in probabilistic latent semantic analysis

Multimedia Tools and Applications - In order to effectively solve the problem of new items and obviously improve the accuracy of the recommended results, we proposed a collaborative recommendation...     

A multi-attribute probabilistic matrix factorization model for personalized recommendation

Recommendation systems can interpret personal preferences and recommend the most relevant choices to the benefit of countless users. Attempts to improve the performance of recommendation systems have hence been the focus of much research in an era of information explosion. As users would like to ask about shopping information with their friend in real life and plentiful information concerning items can help to improve the recommendation accuracy, traditional work on recommending based on users’ social relationships or the content of item tagged by users fails as recommending process relies on mining a user’s historical information as much as possible. This paper proposes a new recommending model incorporating the social relationship and content information of items (SC) based on probabilistic matrix factorization named SC-PMF (Probabilistic Matrix Factorization with Social relationship and Content of items). Meanwhile, we take full advantage of the scalability of probabilistic matrix factorization, which helps to overcome the often encountered problem of data sparsity. Experiments demonstrate that SC-PMF is scalable and outperforms several baselines (PMF, LDA, CTR, SocialMF) for recommending.     

基于CTM-PMF模型的物品推荐

为了克服传统协同过滤推荐技术的局限,提出了一种基于CTM-PMF模型的物品推荐方法。在PMF模型的基础上,引入CTM模型,将PMF模型良好的推荐品质和CTM模型优越的物品表示方法相结合,有效地实现了新物品推荐;通过引入用户兴趣因子,解决了用户对已购买物品的兴趣变化问题。在自建的物品数据集上,利用提出的方法、PMF模型、G-PLSA模型和UBCF方法进行了对比实验,实验结果表明该方法具有良好的物品推荐品质。     

基于信誉的peer-to-peer推荐信任模型

随着对等网络p2p技术的不断发展,如何在p2p各个对等点之间建立起信任关系,已成为当今p2p技术研究的一个重要课题。在研究一些现有信任模型的基础上,分析其存在的问题,提出一种基于信誉的对等网信任模型,给出了信任度计算的算法．并设计了一种信任查询协议,最后,通过实验验证和分析了模型的可行性和安全性．     

基于多Agent的网络学习智能推荐模型

针对网络学习者面临海量信息选择的困扰,提出了一个基于多Agent的网络学习智能推荐模型.运用界面Agent采实现与学习者的交互,利用基于知识推荐的Agent提供与学习者兴趣相关的推荐,以及基于相似学习者推荐的Agent向特定学习者推荐新的知识,并对模型中推荐的相似度算法进行了阐述.通过多Agent技术的运用,较好的解决了网络学习推荐的智能化,个性化以及灵活性的问题,使网络学习者能在一种交互式的学习环境中得到更人性化的学习推荐服务.     

基于认知复杂度度量的文本推荐模型

提出一种基于认知复杂度度量的文本推荐模型。已有的认知复杂度评价方法主要用于评价单一文本的认知复杂度,对此方法进行拓展,将它用于文本集的认知复杂度评价。在推荐模型中,通过对用户查看文章序列的分析,然后使用文本集认知复杂度评价方法查找文章进行推荐,使得用户获得的推荐文本集合更符合认知的规律,更易于理解。实验结果表明文本集认知复杂度评价方法的合理性,并通过比较说明了使用这种推荐模型将使用户获得更加易于理解的文本推荐集合。     

Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation

World Wide Web - The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective...     

License plate localization based on a probabilistic model

Extraction of the license plate region is the challenging first step in the license plate recognition system. We propose a novel feature fusion concept for plate extraction. The image-feature extraction process is modeled as a feature-detection problem in noise. The geometric features are probabilistically modeled and detected under various detection thresholds. These detection results are then fused within the Bayesian framework to obtain the features for further processing. Along with a probabilistic model, a pixels voting algorithm is also tested through threshold variation.     

A probabilistic reputation model based on transaction ratings

This work introduces a probabilistic model allowing to compute reputation scores as close as possible to their intrinsic value, according to the model. It is based on the following, natural, consumer-provider interaction model. Consumers are assumed to order items from providers, who each has some intrinsic, latent, “quality of service” score. In the basic model, the providers supply the items with a quality following a normal law, centered on their intrinsic “quality of service”. The consumers, after the reception and the inspection of the item, rate it according to a linear function of its quality - a standard regression model. This regression model accounts for the bias of the consumer in providing ratings as well as his reactivity towards changes in item quality. Moreover, the constancy of the provider in supplying an equal quality level when delivering the items is estimated by the standard deviation of his normal law of item quality generation. Symmetrically, the consistency of the consumer in providing similar ratings for a given quality is quantified by the standard deviation of his normal law of ratings generation. Two extensions of this basic model are considered as well: a model accounting for truncation of the ratings and a Bayesian model assuming a prior distribution on the parameters. Expectation-maximization algorithms, allowing to estimate the parameters based on the ratings, are developed for all the models. The experiments suggest that these models are able to extract useful information from the ratings, are robust towards adverse behaviors such as cheating, and are competitive in comparison with standard methods. Even if the suggested models do not show considerable improvements over other competing models (such as Brockhoff and Skovgaard’s model [12]), they, however, also permit to estimate interesting features over the raters - such as their reactivity, bias, consistency, reliability, or expectation.     

基于混合型的Web实时推荐模型研究

针对以往个性化网站实时推荐系统存在很难预测用户未来浏览页面的不足,提出了一个混合型的实时推荐模型。该模型将动态模糊聚类技术和改进的关联规则相结合,既挖掘用户与页面的相似度权值形成知识库,又考虑用户的访问事务集增量构造访问模式树,通过修剪其相关分枝,快速生成候选推荐集,由推荐引擎附加在请求页面的底部,在不干扰用户的访问同时,又将用户感兴趣的内容推荐给用户。实验结果表明,该方法能有效地提高推荐的精确率和覆盖率以及综合评价指标。     

Group recommendation based on a bidirectional tensor factorization model

Capturing the preference of virtual groups that consist of a set of users with diversified preference helps recommend targeted products or services in social network platform. Existing strategies for capturing group preference are to directly aggregate individual preferences. Such methods model the preference formation of a group as a unidirectional procedure without considering the influence of the group on individual’s interest. In the context of social group, however, the preference formation is a bidirectional procedure because group preference and individual interest are interrelated. In addition, the influence of group on individuals is usually distinct among users. To address these issues, this paper models the group recommendation problem as a bidirectional procedure and proposes a Bidirectional Tensor Factorization model for Group Recommendation (BTF-GR) to capture the interaction between individual’s intrinsic interest and group influence. A Bayesian personalized ranking technique is employed to learn parameters of the proposed BTF-GR model. Empirical studies on two real-world data sets demonstrate that the proposed model outperforms the baseline algorithms such as matrix factorization for implicit feedback and Bayesian personalized ranking.     

基于用户的协作过滤信息推荐模型研究

当网络成为人们获取信息的主要途径时,"信息过量"与"信息饥饿"的矛盾却日益凸现,因此,提供个性化服务显得尤为必要.提出了一种基于用户的协作过滤信息推荐模型,实验结果表明,该模型能够有效地改善传统协作过滤推荐技术所面临的扩展性和数据高维稀疏性问题,同时信息推荐质量较传统推荐算法还有明显提高.     

一种基于概率的超声波模型的设计

超声波检测迅速、方便、计算简单、易于做到实时控制,且其测量精度能达到工业实用的要求。在超声波硬件电路基础上,重点对声纳的测量特性及其信息不确定性进行了分析,并提出了一种新的超声波传感器模型。该模型在均匀分布超声波模型的基础上,利用概率方法能准确描述声纳扇形范围之内的障碍物位置,通过仿真验证了该模型的有效性和实用性。     

A probabilistic object tracking model based on condensation algorithm

Object tracking, which has many application in our daily life, is an important topic in electronics engineering area. It basically deals with estimation and location of an object in given video frames. In this paper, a novel object tracking algorithm based on particle filtering associate with population balances is proposed. The developed algorithm was used to track objects in synthetic frames and natural video frames. According to results, it has high accuracy level for single and multi-object tracking.     

基于混合概率背景模型的视频分割方法

提出一种新的基于混合概率模型的背景建模方法,用于视频中前景物体的检测与分割。主要利用两个概率模型：隐马尔可夫模型和概率图模型建立一个混合的贝叶斯网概率模型,对视频输入中背景变化的时间和空间局部相关性(同现性)进行学习。在建立正确模型参数的基础上,贝叶斯信念传播算法根据图像输入预测当前背景状态的后验分布,并根据预测得到的背景状态对输入图像进行分割。实验结果验证了该方法的有效性和在复杂背景变化下的鲁棒性。     

Image recommendation based on a novel biologically inspired hierarchical model

Image recommendation has become an increasingly relevant problem recently, since strong demand to quickly find interested images from vast amounts of image library. We describe a biologically inspired hierarchical model for image recommendation. The biologically inspired model (BIM) for invariant feature representation has attracted widespread attention, which approximately follows the organization of cortex visuel. BIM is a computation architecture with four layers. With the image data size increases, the four-layer framework is prone to be overfitting, which limits its application. To address this issue, we propose a biologically inspired hierarchical model (BIHM) for feature representation, which adds two more discriminative layers upon the conventional four-layer framework. In contrast to the conventional BIM that mimics the inferior temporal cortex, which corresponds to the low level feature, the proposed BIHM adds two more layers upon the conventional framework to simulate inferotemporal cortex, exploring higher level feature invariance and selectivity. Furthermore, we firstly utilize the BIHM in the image recommendation. To demonstrate the effectiveness of proposed model, we use it to image classification and retrieval tasks and perform experiments on CalTech5, Imagenet and CalTech256 datasets. The experiment results show that BIHM exhibits better performance than the conventional model in the tasks and is very comparable to existing architectures.     

基于双维度云模型的协同过滤推荐算法

随着网络的迅速普及,网络应用多样化趋势加快,大数据时代已经到来,电商业对于在线推荐系统的要求也越来越高。为了满足人们的需求,传统的推荐算法必须得到改进和发展。本文主要考虑数据比较稀疏及数据规模较大的情况下如何更好地实现协同过滤推荐算法,针对这两个方面的不足,结合Hadoop平台的优势,基于双维度云模型的协同过滤推荐算法由此应运而生了。通过实验,利用云模型和双维度数据,有关数据稀疏性问题得到了合理的解决,预测准确率方面也得到了一定的改进。并且通过MapReduce处理流程,使大数据环境下运行困难效率低下的缺陷得到了弥补。     

POI recommendation based on a multiple bipartite graph network model

The Journal of Supercomputing - In location-based social network platforms, the point-of-interest(POI) recommendation is an essential function to serve users. The existing POI recommendation...