基于灰色关联聚类的协同过滤推荐算法

针对协同过滤推荐系统具有数据的高稀疏,高维度,数据量大的特点,本文将灰色关联聚类与协同过虑推荐算法相结合,构建了灰色关联聚类的协同过滤推荐算法,将其应用到协同过滤推荐系统中,以解决数据具有高稀疏高维度的特性情况下的个性化推荐质量问题。首先,定义了推荐系统中的用户项目评分矩阵,用户灰色绝对关联度,用户灰色相似度,用户灰色关联聚类。然后,给出了灰色关联聚类的协同过滤推荐算法的计算方法和步骤,同时给出了评价推荐质量方法。最后,将本文算法与基于余弦,相关分析及修正的余弦等协同过滤推荐算法在大小不同的数据集下进行了实验,实验表明灰色关联聚类的协同过滤推荐算法相较于传统的协同过滤推荐方法具有推荐质量高,计算量小,对数据大小要求不高等优点,同时在推荐系统的冷启动,稳定性和计算效率方面也具有一定的优势。     

个性化推荐系统中协同过滤推荐算法优化研究

协同过滤推荐算法是目前个性化推荐系统中应用比较广泛的一种算法。然而,它在处理数据稀疏性、可扩展性等方面存在一定不足。针对数据稀疏性问题,本文首先基于Slope One算法对初始的评分矩阵进行缺失值填充,其次利用基于K-means聚类的协同过滤算法预测目标用户的评分,并结合MovieLens数据集给出了相关对比实验;针对扩展性问题,本文首先提出了一种基于中心聚集参数的改进K-means算法,其次,给出了基于中心聚集参数改进K-means的协同过滤推荐算法流程,并结合MovieLens数据集设计了相关对比实验。实验结果表明,本文所提方法推荐精度均得到显著提高,数据稀疏性和扩展性问题得到了有效改善。因此,本文的研究结论不仅可进一步丰富协同过滤推荐算法的现有理论成果,还可以为提高推荐系统的精度提供理论依据和决策参考。     

Qualitative chain graphs and their application

For many problem domains, such as medicine, chain graphs are more attractive than Bayesian networks as they support representing interactions between variables that have no natural direction. In particular, interactions between variables that result from certain feedback mechanisms can be represented by chain graphs. Using qualitative abstractions of probabilistic interactions is also of interest, as these allow focusing on patterns in the interactions rather than on the numerical detail. Such patterns are often known by experts and sufficient for making decisions. So far, qualitative abstractions of probabilistic interactions have only been developed for Bayesian networks in the form of qualitative probabilistic networks. In this paper, such qualitative abstractions are developed for chain graphs with the practical aim of using qualitative knowledge as constraints on the hyperspace of probability distributions. The usefulness of qualitative chain graphs is explored for modelling and reasoning about the interactions between diseases.     

基于聚类矩阵近似的协同过滤推荐研究

由于推荐系统中存在巨量的用户和商品,现有的协同过滤方法很难处理用户-商品推荐中的数据稀疏性和计算可扩展性问题。本文提出了一种基于聚类矩阵近似的协同过滤推荐方法CF-cluMA。一方面,CF-cluMA方法通过对用户和商品进行分别聚类,并利用聚类后的用户-商品分块评分矩阵来刻画用户对于商品兴趣的局部性特点,以降低用户-商品评分矩阵的全局稀疏性。另一方面,CF-cluMA方法通过对局部稠密分块矩阵实施奇异值分解,并利用施密特变换近似全局用户-商品评分矩阵来预测用户对未知商品评分,以降低协同过滤算法的复杂性。在EachMovie电影评分真实数据集上的实验表明,相比于已有的基于矩阵近似的协同过滤推荐方法,本文所提出的CF-cluMA方法能够有效提升推荐系统的准确性并降低推荐系统的计算复杂性。本文的研究对于电子商务推荐系统具有重要的管理启示。     

On solving the forward kinematics of 3RPR planar parallel manipulator using hybrid metaheuristics

Hybrid metaheuristics have been applied with success in solving many real-world problems. This work introduces hybrid metaheuristics to the field of kinematics problem, in particular, for solving the forward kinematics of the 3RPR parallel manipulator. It implements a combination of genetic algorithms and simulated annealing into two popular hybrid metaheuristic techniques. They are combined as teamwork and relay collaborative hybrid metaheuristics and compared to the performance of genetic algorithms and simulated annealing alone. The results show that the meta-heuristic approaches give robust and high quality solutions. Genetic algorithms and teamwork collaborative metaheuristics showed better performance than simulated annealing and relay collaborative metaheuristics. The given metaheuristic methods obtain all the unique solutions and comparisons with algebraic methods show promising results.     

Forecasting nonlinear time series with a hybrid methodology

In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible to model both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybrid approach combining Elman’s Recurrent Neural Networks (ERNN) and ARIMA models is proposed. The proposed hybrid approach is applied to Canadian Lynx data and it is found that the proposed approach has the best forecasting accuracy.     

A latent model for collaborative filtering

Recommender systems based on collaborative filtering have received a great deal of interest over the last two decades. In particular, recently proposed methods based on dimensionality reduction techniques and using a symmetrical representation of users and items have shown promising results. Following this line of research, we propose a probabilistic collaborative filtering model that explicitly represents all items and users simultaneously in the model. Experimental results show that the proposed system obtains significantly better results than other collaborative filtering systems (evaluated on the MovieLens data set). Furthermore, the explicit representation of all users and items allows the model to e.g. make group-based recommendations balancing the preferences of the individual users.     

Decision-theoretic troubleshooting: Hardness of approximation

Decision-theoretic troubleshooting is one of the areas to which Bayesian networks can be applied. Given a probabilistic model of a malfunctioning man-made device, the task is to construct a repair strategy with minimal expected cost. The problem has received considerable attention over the past two decades. Efficient solution algorithms have been found for simple cases, whereas other variants have been proven NP-complete. We study several variants of the problem found in literature, and prove that computing approximate troubleshooting strategies is NP-hard. In the proofs, we exploit a close connection to set-covering problems.     

The reliability analysis of probabilistic and interval hybrid structural system

The aim of this paper is to evaluate the reliability of probabilistic and interval hybrid structural system. The hybrid structural system includes two kinds of uncertain parameters—probabilistic parameters and interval parameters. Based on the interval reliability model and probabilistic operation, a new probabilistic and interval hybrid reliability model is proposed. Firstly, we use the interval reliability model to analyze the performance function, and then sum up reliability of all regions divided by the failure plane. Based on the presented optimal criterion enumerating the main failure modes of hybrid structural system and the relationship of failure modes, the reliability of structure system can be obtained. By means of the numerical examples, the hybrid reliability model and the traditional probabilistic reliability model are critically contrasted. The results indicate the presented reliability model is more suitable for analysis and design of these structural systems and it can ensure the security of system well, and it only needs less uncertain information.     

Multi-dimensional classification with Bayesian networks

Multi-dimensional classification aims at finding a function that assigns a vector of class values to a given vector of features. In this paper, this problem is tackled by a general family of models, called multi-dimensional Bayesian network classifiers (MBCs). This probabilistic graphical model organizes class and feature variables as three different subgraphs: class subgraph, feature subgraph, and bridge (from class to features) subgraph. Under the standard 0-1 loss function, the most probable explanation (MPE) must be computed, for which we provide theoretical results in both general MBCs and in MBCs decomposable into maximal connected components. Moreover, when computing the MPE, the vector of class values is covered by following a special ordering (gray code). Under other loss functions defined in accordance with a decomposable structure, we derive theoretical results on how to minimize the expected loss. Besides these inference issues, the paper presents flexible algorithms for learning MBC structures from data based on filter, wrapper and hybrid approaches. The cardinality of the search space is also given. New performance evaluation metrics adapted from the single-class setting are introduced. Experimental results with three benchmark data sets are encouraging, and they outperform state-of-the-art algorithms for multi-label classification.     

融入项目类别信息的协同过滤推荐算法

协同过滤技术在电子商务领域得到了广泛的研究和应用,但是随着互联网的迅速普及和电子商务网站规模的急剧增长,用户评分的极端稀疏性导致协同过滤方法的推荐质量不高.提出了一种融入项目类别信息的协同过滤推荐算法,结合项目的类别信息为活动用户筛选出候选近邻集合,在候选近邻集合内综合利用项目的评分信息和类别信息对未评分值进行预测,最后依据用户实际评分和预测评分计算出活动用户的最近邻集合并进行推荐.实验结果表明,该算法具有较好的推荐准确性和实时性.     

Inference of an oscillating model for the yeast cell cycle

High-throughput techniques allow measurement of hundreds of cell components simultaneously. The inference of interactions between cell components from these experimental data facilitates the understanding of complex regulatory processes. Differential equations have been established to model the dynamic behavior of these regulatory networks quantitatively. Usually traditional regression methods for estimating model parameters fail in this setting, since they overfit the data. This is even the case, if the focus is on modeling subnetworks of, at most, a few tens of components. In a Bayesian learning approach, this problem is avoided by a restriction of the search space with prior probability distributions over model parameters.This paper combines both differential equation models and a Bayesian approach. We model the periodic behavior of proteins involved in the cell cycle of the budding yeast Saccharomyces cerevisiae, with differential equations, which are based on chemical reaction kinetics. One property of these systems is that they usually converge to a steady state, and lots of efforts have been made to explain the observed periodic behavior. We introduce an approach to infer an oscillating network from experimental data. First, an oscillating core network is learned. This is extended by further components by using a Bayesian approach in a second step. A specifically designed hierarchical prior distribution over interaction strengths prevents overfitting, and drives the solutions to sparse networks with only a few significant interactions.We apply our method to a simulated and a real world dataset and reveal main regulatory interactions. Moreover, we are able to reconstruct the dynamic behavior of the network.     

数据稀疏环境下基于用户主题偏好的协同过滤算法

在B2C电子商务中,user-based协同过滤算法是一种重要的推荐方法,但用户共同评价项目数据稀疏影响了user-based协同过滤算法的应用。鉴于此,在考虑用户消费水平的基础上,利用关联规则挖掘形式化描述商品间的替代相似性;利用基于时间的贝叶斯概率描述商品间的关联关系构建商品网络,通过社会网络分析中的成份分析方法对商品网分析,得到面向用户主题偏好的商品间互补性关系,进而利用这两种商品间关系构建用户主题偏好项目集,最后在数据极度稀疏的环境下通过F₁方法和多样性测量方法与传统推荐算法进行对比实验分析,实验结果显示提高了推荐结果的准确性与新颖性。研究用的所有数据均采集于京东商城网站。本文为缓解数据稀疏问题提出了一种新的方法,扩展了整体网分析方法在商品关系分析中的应用,含有理论与实践双重意义。     

A new memetic algorithm based on cellular learning automata for solving the vertex coloring problem

Vertex coloring problem is a combinatorial optimization problem in graph theory in which a color is assigned to each vertex of graph such that no two adjacent vertices have the same color. In this paper a new hybrid algorithm which is obtained from combination of cellular learning automata (CLA) and memetic algorithm (MA) is proposed for solving the vertex coloring problem. CLA is an effective probabilistic learning model combining cellular automata and learning automaton (LA). Irregular CLA (ICLA) is a generalization of CLA in which the restriction of rectangular grid structure in CLA is removed. The proposed algorithm is based on the irregular open CLA (IOCLA) that is an extension of ICLA in which the evolution of CLA is influenced by both local and global environments. Similar to other IOCLA-based algorithms, in the proposed algorithm, local environment is constituted by neighboring LAs of any cell and the global environment consists of a pool of memes in which each meme corresponds to a certain local search method. Each meme is represented by a set of LAs from which the history of the corresponding local search method can be extracted. To show the superiority of the proposed algorithm over some well-known algorithms, several computer experiments have been conducted. The results show that the proposed algorithm performs better than other methods in terms of running time of algorithm and the required number of colors.     

Structural-EM for learning PDG models from incomplete data

Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by other popular models, such as Bayesian Networks. Furthermore, inference can be carried out efficiently over a PDG, in time linear in the size of the model. The problem of learning PDGs from data has been studied in the literature, but only for the case of complete data. We propose an algorithm for learning PDGs in the presence of missing data. The proposed method is based on the Expectation-Maximisation principle for estimating the structure of the model as well as the parameters. We test our proposal on both artificially generated data with different rates of missing cells and real incomplete data. We also compare the PDG models learnt by our approach to the commonly used Bayesian Network (BN) model. The results indicate that the PDG model is less sensitive to the rate of missing data than BN model. Also, though the BN models usually attain higher likelihood, the PDGs are close to them also in size, which makes the learnt PDGs preferable for probabilistic inference purposes.     

Combined analysis of unique and repetitive events in quantitative risk assessment

For risk assessment to be a relevant tool in the study of any type of system or activity, it needs to be based on a framework that allows for jointly analyzing both unique and repetitive events. Separately, unique events may be handled by predictive probability assignments on the events, and repetitive events with unknown/uncertain frequencies are typically handled by the probability of frequency (or Bayesian) approach. Regardless of the nature of the events involved, there may be a problem with imprecision in the probability assignments. Several uncertainty representations with the interpretation of lower and upper probability have been developed for reflecting such imprecision. In particular, several methods exist for jointly propagating precise and imprecise probabilistic input in the probability of frequency setting. In the present position paper we outline a framework for the combined analysis of unique and repetitive events in quantitative risk assessment using both precise and imprecise probability. In particular, we extend an existing method for jointly propagating probabilistic and possibilistic input by relaxing the assumption that all events involved have frequentist probabilities; instead we assume that frequentist probabilities may be introduced for some but not all events involved, i.e. some events are assumed to be unique and require predictive – possibly imprecise – probabilistic assignments, i.e. subjective probability assignments on the unique events without introducing underlying frequentist probabilities for these. A numerical example related to environmental risk assessment of the drilling of an oil well is included to illustrate the application of the resulting method.     

Integrating Bayesian networks and decision trees in a sequential rule-based transportation model

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. Some of these models use decision rules to support its decision-making instead of principles of utility maximization. Decision rules can be derived from different modelling approaches. In a previous study, it was shown that Bayesian networks outperform decision trees and that they are better suited to capture the complexity of the underlying decision-making. However, one of the disadvantages is that Bayesian networks are somewhat limited in terms of interpretation and efficiency when rules are derived from the network, while rules derived from decision trees in general have a simple and direct interpretation. Therefore, in this study, the idea of combining decision trees and Bayesian networks was explored in order to maintain the potential advantages of both techniques. The paper reports the findings of a methodological study that was conducted in the context of Albatross, which is a sequential rule based model of activity scheduling behaviour. To this end, the paper can be situated within the context of a series of previous publications by the authors to improve decision-making in Albatross. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of Albatross with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.     

Probabilistic verification of a biodiesel production system using statistical model checking

Biochemical system designers are increasingly using formal modelling, simulation, and verification methods to improve the understanding of complex systems. Probabilistic models can incorporate realistic stochastic dynamics, but creating and analysing probabilistic models in a formal way is challenging. In this work, we present a stochastic model of biodiesel production that incorporates an inexpensive test of fuel quality, and we validate the model using statistical model checking, which can be used to evaluate simple or complex temporal properties efficiently. We also describe probabilistic simulation and analysis techniques for stochastic hybrid system (SHS) models to demonstrate the properties of our model. We introduce a variety of properties for various configurations of the reactor as well as results of testing our model against the properties.     

An anytime deduction algorithm for the probabilistic logic and entailment problems

We study two basic problems of probabilistic reasoning: the probabilistic logic and the probabilistic entailment problems. The first one can be defined as follows. Given a set of logical sentences and probabilities that these sentences are true, the aim is to determine whether these probabilities are consistent or not. Given a consistent set of logical sentences and probabilities, the probabilistic entailment problem consists in determining the range of the possible values of the probability associated with additional sentences while maintaining a consistent set of sentences and probabilities.This paper proposes a general approach based on an anytime deduction method that allows the follow-up of the reasoning when checking consistency for the probabilistic logic problem or when determining the probability intervals for the probabilistic entailment problem. Considering a series of subsets of sentences and probabilities, the approach proceeds by computing increasingly narrow probability intervals that either show a contradiction or that contain the tightest entailed probability interval. Computational experience have been conducted to compare the proposed anytime deduction method, called ad-psat with an exact one, psatcol, using column generation techniques, both with respect to the range of the probability intervals and the computing times.     

On cautious probabilistic inference and default detachment

Conditional probabilities are one promising and widely used approach to model uncertainty in information systems. This paper discusses the DUCK-calculus, which is founded on the cautious approach to uncertain probabilistic inference. Based on a set of sound inference rules, derived probabilistic information is gained by local bounds propagation techniques. Precision being always a central point of criticism to such systems, we demonstrate that DUCK need not necessarily suffer from these problems. We can show that the popular Bayesian networks are subsumed by DUCK, implying that precise probabilities can be deduced by local propagation techniques, even in the multiply connected case. A comparative study with INFERNO and with inference techniques based on global operations-research techniques yields quite favorable results for our approach. Since conditional probabilities are also suited to model nonmonotonic situations by considering different contexts, we investigate the problems of maximal and relevant contexts, needed to draw default conclusions about individuals.