The performance of recommender systems in online shopping: A user-centric study

This research investigates the effects of preference relaxation on decision-making performance of users in online preference-based product search contexts. We compare four recommender systems based on different preference relaxation methods in extensive user experiments with 111 subjects that use two real-world datasets: 1818 digital cameras and 45,278 used car advertisements gathered from popular e-commerce websites. Our results provide new insights into the positive impact of the Soft-Boundary Preference Relaxation methods on decision-making quality and effort. The paper extends previous studies on this topic and demonstrates that decision aids based on preference relaxation techniques can effectively enhance preference-based product search in online product catalogues and help alleviate common disadvantages of form-based filtering mechanisms.     

基于数据挖掘的CRM分析

进行客户关系管理系统建设,是企业争取竞争优势的重要手段,数据挖掘技术在CRM的实施中起着关键的作用。文章介绍了数据挖掘技术和CRM技术,具体介绍了在酒店CRM建设中用到的决策树和模糊聚类这两种数据挖掘的实现方法,并做出了实验分析。     

贝叶斯决策树在客户流失预测中的应用

针对电信企业客户流失问题,提出采用贝叶斯决策树算法的预测模型,将贝叶斯分类的先验信息方法与决策树分类的信息熵增益方法相结合,应用到电信行业客户流失分析中,分别将移动公司的客户数据以及UCI数据纳入到模型中得出相应的结果。加入贝叶斯节点弥补决策树不能处理缺失值以及二义性数据的缺点。检验结果表明,基于贝叶斯推理的决策树算法在牺牲了较小的训练时间与分类时间的情况下,得到了比仅基于决策树算法更高的覆盖率与命中率。     

数据挖掘技术在民航CRM中的应用

进行客户关系管理系统建设,是当今各大航空公司争取竞争优势的重要手段。数据挖掘技术在CRM的实施中起着关键的作用。文章分别介绍了数据挖掘技术和CRM技术,具体介绍了在民航CRM建设中用到的决策树方法和模糊聚类方法这两种数据挖掘方法,并作了实验分析。最后对数据挖掘技术在民航中的应用作了展望。     

A multi-agent legal recommender system

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology.     

VDM-RS: A visual data mining system for exploring and classifying remotely sensed images

Remotely sensed imagery has become increasingly important in several applications domains, such as environmental monitoring, change detection, fire risk mapping and land use, to name only a few. Several advanced image classification techniques have been developed to analyze such imagery and in particular to improve the accuracy of classifying images in the context of such applications. However, most of the proposed classifiers remain a black box to users, leaving them with little to no means to explore and thus further improve the classification process, in particular for misclassified pixel samples. In this paper, we present the concepts, design and implementation of VDM-RS, a visual data mining system for classifying remotely sensed images and exploring image classification processes. The system provides users with two classes of components. First, visual components are offered that are specific to classifying remotely sensed images and provide traditional interfaces, such as a map view and an error matrix view. Second, the decision tree classifier view provides users with the functionality to trace and explore the classification process of individual pixel samples. This feature allows users to inspect how a sample has been correctly classified using the classifier, but more importantly, it also allows for a detailed exploration of the steps in which a sample has been misclassified. The integration of these features into a coherent, user-friendly system not only helps users in getting more insights into the data, but also to better understand and subsequently improve a classifier for remotely sensed images. We demonstrate the functionality of the system's components and their interaction for classifying imagery using a hyperspectral image dataset.     

A multi-disciplinar recommender system to advice research resources in University Digital Libraries

The Web is one of the most important information media and it is influencing in the development of other media, as for example, newspapers, journals, books, and libraries. In this paper, we analyze the logical extensions of traditional libraries in the Information Society. In Information Society people want to communicate and collaborate. So, libraries must develop services for connecting people together in information environments. Then, the library staff need automatic techniques to facilitate so that a great number of users can access to a great number of resources. Recommender systems are tools whose objective is to evaluate and filter the great amount of information available on the Web to assist the users in their information access processes. We present a model of a fuzzy linguistic recommender system to help the University Digital Libraries users to access for their research resources. This system recommends researchers specialized and complementary resources in order to discover collaboration possibilities to form multi-disciplinar groups. In this way, this system increases social collaboration possibilities in a university framework and contributes to improve the services provided by a University Digital Library.     

Learning with personalized recommender systems: A psychological view

This paper explores the potentials of recommender systems for learning from a psychological point of view. It is argued that main features of recommender systems (collective responsibility, collective intelligence, user control, guidance, personalization) fit very well to principles in the learning sciences. However, recommender systems should not be transferred from commercial to educational contexts on a one-to-one basis, but rather need adaptations in order to facilitate learning. Potential adaptations are discussed both with regard to learners as recipients of information and learners as producers of data. Moreover, it is distinguished between system-centered adaptations that enable proper functioning in educational contexts, and social adaptations that address typical information processing biases. Implications for the design of educational recommender systems and for research on educational recommender systems are discussed.     

Alambic: a privacy-preserving recommender system for electronic commerce

Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. Customers should be able to keep private their personal information, including their buying preferences, and they should not be tracked against their will. The commercial interests of merchants should also be protected by allowing them to make accurate recommendations without revealing legitimately compiled valuable information to third parties. We introduce a theoretical approach for a system called Alambic, which achieves the above privacy-protection objectives in a hybrid recommender system that combines content-based, demographic and collaborative filtering techniques. Our system splits customer data between the merchant and a semi-trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system could only be subverted by a coalition between these two parties.

A google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0

Nowadays Digital Libraries 2.0 are mainly based on the interaction between users through collaborative applications such as wikis, blogs, etc. or new possible paradigms like the waves proposed by Google. This new concept, the wave, represents a common space where resources and users can work together. The problem arises when the number of resources and users is high, then tools for assisting the users in their information needs are necessary. In this case a fuzzy linguistic recommender system based on the Google Wave capabilities is proposed as tool for communicating researchers interested in common research lines. The system allows the creation of a common space by means a wave as a way of collaborating and exchanging ideas between several researchers interested in the same topic. In addition, the system suggests, in an automatic way, several researchers and useful resources for each wave. These recommendations are computed following several previously defined preferences and characteristics by means of fuzzy linguistic labels. Thus the system facilitates the possible collaborations between multi-disciplinar researchers and recommends complementary resources useful for the interaction. In order to test the effectiveness of the proposed system, a prototype of the system has been developed and tested with several research groups from the same university achieving successful results.     

Classification trees and decision-analytic feedforward control: a case study from the video game industry

The objective of this paper is to use a challenging real-world problem to illustrate how a probabilistic predictive model can provide the foundation for decision-analytic feedforward control. Commercial data mining software and sales data from a market research firm are used to create a predictive model of market success in the video game industry. A procedure is then described for transforming the classification trees into a decision-analytic model that can be solved to produce a value-maximizing game development policy. The video game example shows how the compact predictive models created by data mining algorithms can help to make decision-analytic feedforward control feasible, even for large, complex problems. However, the example also highlights the bounds placed on the practicality of the approach due to combinatorial explosions in the number of contingencies that have to be modeled. We show, for example, how the “option value” of sequels creates complexity that is effectively impossible to address using conventional decision analysis tools.     

浅析数据挖掘技术

本文主要讨论了数据挖掘方法的研究发展,简要地介绍了数据挖掘的定义、功能、方法等,详细的介绍了一些常用的数据挖掘技术,包括概念,应用范围,选择划分的标准等。     

一种与神经元网络杂交的决策树算法

神经元网络在多数情况下获得的精度要比决策树和回归算法精度高,这是因为它能适应更复杂的模型,同时由于决策树通常每次只使用一个变量来分支,它所对应的识别空间只能是超矩形,这也就比神经元网络简单,粗度不能与神经元网络相比,然而神经元网络需要相对多的学习时间,并且其模型的可理解性不如决策树、Naive-Bayes等方法直观,本文在进行两种算法对复杂模型的识别对比后,提出了一个新的算法NNTree,这是一个决策树和神经元网络杂交的算法,决策树节点包含单变量的分支就象正常的决策树,但是叶子节点包含神经元网络分类器,这个方法针对决策树处理大型数据的效能,保留了决策树的可理解性,改善了神经元网络的学习性能,同时可使这个分类器的精度大大超过这两种算法,尤其在测试更大的数据集复杂模型时更为明显。     

Collaborative recommender systems: Combining effectiveness and efficiency

Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the “big picture”, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms.     

一种两阶段决策树建树方法及其应用

提出一种新颖的两阶段决策树建树方法;在对数据集进行较粗的分类后,通过遗传算法寻找规则集来建立决策树叶子节点.该方法可以同时对多个属性进行度量,并避免了决策树的剪枝过程。     

Evaluation of recommender systems: A new approach

It is difficult to deny that comparison between recommender systems requires a common way for evaluating them. Nevertheless, at present, they have been evaluated in many, often incompatible, ways. We affirm this problem is mainly due to the lack of a common framework for recommender systems, a framework general enough so that we may include the whole range of recommender systems to date, but specific enough so that we can obtain solid results. In this paper, we propose such a framework, attempting to extract the essential features of recommender systems. In this framework, the most essential feature is the objective of the recommender system. What is more, in this paper, recommender systems are viewed as applications with the following essential objective. Recommender systems must: (i) choose which (of the items) should be shown to the user, (ii) decide when and how the recommendations must be shown. Next, we will show that a new metric emerges naturally from this framework. Finally, we will conclude by comparing the properties of this new metric with the traditional ones. Among other things, we will show that we may evaluate the whole range of recommender systems with this single metric.     

Intelligent tourism recommender systems: A survey

Recommender systems are currently being applied in many different domains. This paper focuses on their application in tourism. A comprehensive and thorough search of the smart e-Tourism recommenders reported in the Artificial Intelligence journals and conferences since 2008 has been made. The paper provides a detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques. The survey also provides some guidelines for the construction of tourism recommenders and outlines the most promising areas of work in the field for the next years.     

A recommender system applied to the indirect materials selection process (RS-IMSP) for producing automobile spare parts

In this work, the development of a recommender system that aims to facilitate the indirect materials selection task for the creation of spare parts is proposed. In the industrial sector there are spare parts manufacturing companies, where there is a high rotation of staff and this leads to loss of knowledge as new users do not know what indirect materials they should select in the warehouse to create certain parts. The proposed system aims to integrate an indirect materials recommender system to assist this warehouse task. The proposed system is based on the non-personalized approach and similar order circumstances, to perform the recommendation process. From the evaluation of the proposed system, we could conclude that the indirect materials selection process for producing auto parts was improved.     

跳频通信系统的异常跳变故障检测模型仿真

跳频通信系统的异常跳变故障检测关系到系统的安全.大型跳频通信系统中,系统处在频率跳动的环境中,不同频率区域的系统之间的频率变换幅度不同,不同系统的数据异常跳变特征有着不同的频率判断标准.传统的跳频通信系统的异常跳变故障检测方法在进行异常数据检测时,以不同区域多个不同固定频率阀值特征进行衡量,没有考虑跳频系统的频率时变特殊性,对故障检测以固定频率特征判断,很容易出现误判.提出采用决策树挖掘算法的跳频通信系统的异常跳变故障检测方法.针对跳频通信系统的异常跳变故障数据进行样本空间分类处理,获取关联数据聚类目标函数,并对聚类中心进行有效的更新,实现跳频通信系统的异常跳变故障数据的聚类处理.构建上述数据对应的决策树,并计算信息增益比,实现跳频通信系统的异常跳变故障数据的检测.实验结果表明,利用改进算法进行跳频通信系统的异常跳变故障数据的检测,能够极大的提高检测的效率.     

A decision support system for Evidence Based Medicine

We present a decision support system to let medical doctors analyze important clinical data, like patients medical history, diagnosis, or therapy, in order to detect common patterns of knowledge useful in the diagnosis process. The underlying approach mainly exploits case-based reasoning (CBR), which is useful to extract knowledge from previously experienced cases. In particular, we used sequence data mining to detect common patterns in patients histories and to highlight the effects of medical practices, based on evidence.We also exploited data warehousing techniques, such OLAP queries to let medical doctor analyze diagnosis along several measures, and recent visual data integration approaches and tools to effectively support the complex task of integrating and reconciling data from different medical data sources. In addition, due to massive presence of textual information within the clinical records of many hospitals, text mining techniques have been devised. In particular, we performed lexical analysis of free text in order to extract discriminatory terms and to derive encoded information. Finally, the system provides user friendly mechanisms to manage the protection of confidential medical data.System validation has been performed, mainly focusing on usability issues, by running experiments based on a large database from a primary public hospital.