Mining affective needs of automotive industry customers for building a mass-customization recommender system

Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool’s effectiveness, usefulness and reliability among other parameters.     

Efficient Adaptive-Support Association Rule Mining for Recommender Systems

Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a specific target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches.     

基于加权序列模式的推荐算法研究

由于考虑了用户的访问顺序,基于序列模式的推荐方法正在成为推荐系统研究的热点之一。为提高推荐结果的个性化程度,提出了一种基于加权序列模式的推荐算法PRWSP。首先,给出了新的加权序列模式模型,该模型在设置权重时充分考虑了项目在不同序列中的不同重要程度。其次,通过近似估计序列权重的方式,论证了挖掘加权序列模式时同样满足反单调性,从而约简了搜索空间。最后,定义了序列模式匹配程度的度量标准。实验结果表明,PRWSP算法具有较高的挖掘效率和推荐精度。     

An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering

Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe an iterative methodology to develop and carry out the maintenance of web-based courses to which we have added a specific data mining step. We apply association rule mining to discover interesting information through students’ usage data in the form of IF-THEN recommendation rules. We have also used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education. Finally, we have carried out experiments with several real groups of students using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents.     

A context-aware recommender method based on text and opinion mining

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context-aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context-aware recommendation method based on text mining (CARM-TM). The method includes two techniques to extract context from reviews: CIET.5_embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect-based opinions. We call our extension of CARM-TOM (context-aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context-aware recommender based on matrix factorization. To evaluate CARM-TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM-TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context-aware recommender system.     

旅游推荐系统研究综述

为用户提供个性化推荐服务并提高推荐的准确度和用户满意度,是当前旅游推荐系统的主要研究任务。文中分析了旅游推荐系统与传统推荐系统的异同点,并从基于内容的推荐、基于协同过滤的推荐、基于知识的推荐、基于人口统计的推荐、混和型推荐以及基于位置感知的推荐共6个方面考查了旅游推荐的研究现状。在此基础上,给出了旅游推荐系统的一个总体框架。最后,总结分析了旅游推荐系统面临的6个重点和难点问题,并指出了下一步需要关注的研究方向。     

Multistakeholder recommendation: Survey and research directions

Recommender systems provide personalized information access to users of Internet services from social networks to e-commerce to media and entertainment. As is appropriate for research in a field with a focus on personalization, academic studies of recommender systems have largely concentrated on optimizing for user experience when designing, implementing and evaluating their algorithms and systems. However, this concentration on the user has meant that the field has lacked a systematic exploration of other aspects of recommender system outcomes. A user-centric approach limits the ability to incorporate system objectives, such as fairness, balance, and profitability, and obscures concerns that might come from other stakeholders, such as the providers or sellers of items being recommended. Multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article outlines the multistakeholder perspective on recommendation, highlighting example research areas and discussing important issues, open questions, and prospective research directions.

     

Preventing Recommendation Attack in Trust-Based Recommender Systems

Despite its success, similarity-based collaborative filtering suffers from some limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system. To the best of our knowledge, there has not any prior study on recommendation attack in a trust-based recommender system. We analyze the attack problem, and find that “victim” nodes play a significant role in the attack. Furthermore, we propose a data provenance method to trace malicious users and identify the “victim” nodes as distrust users of recommender system. Feasibility study of the defend method is done with the dataset crawled from Epinions website.     

Integrating AHP and data mining for product recommendation based on customer lifetime value

Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers’ needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

Mining Skewed and Sparse Transaction Data for Personalized Shopping Recommendation

A good shopping recommender system can boost sales in a retailer store. To provide accurate recommendation, the recommender needs to accurately predict a customer's preference, an ability difficult to acquire. Conventional data mining techniques, such as association rule mining and collaborative filtering, can generally be applied to this problem, but rarely produce satisfying results due to the skewness and sparsity of transaction data. In this paper, we report the lessons that we learned in two real-world data mining applications for personalized shopping recommendation. We learned that extending a collaborative filtering method based on ratings (e.g., GroupLens) to perform personalized shopping recommendation is not trivial and that it is not appropriate to apply association-rule based methods (e.g., the IBM SmartPad system) for large scale prediction of customers' shopping preferences. Instead, a probabilistic graphical model can be more effective in handling skewed and sparse data. By casting collaborative filtering algorithms in a probabilistic framework, we derived HyPAM (Hybrid Poisson Aspect Modelling), a novel probabilistic graphical model for personalized shopping recommendation. Experimental results show that HyPAM outperforms GroupLens and the IBM method by generating much more accurate predictions of what items a customer will actually purchase in the unseen test data. The data sets and the results are made available for download at http://chunnan.iis.sinica.edu.tw/hypam/HyPAM.html.     

A personalized recommendation procedure for Internet shopping support

The rapid growth of e-commerce has caused product overload where the customer is no longer able to effectively choose the products he/she is exposed to. To overcome the product overload of Internet shoppers, several recommender systems have been developed. Recommendation systems track past actions of a group of customers to make a recommendation to individual members of the group. We introduce a personalized recommendation procedure by which we can get further recommendation effectiveness when applied to Internet shopping malls. The suggested procedure is based on Web usage mining, product taxonomy, association rule mining, and decision tree induction. We applied the procedure to a leading Internet shopping mall in Korea for performance evaluation, and some experimental results are provided. The experimental results show that choosing the right level of product taxonomy and the right customers increases the quality of recommendations.     

Analysis and Classification of Multi-Criteria Recommender Systems

Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there exist several contributions describing recommender systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation, in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding and development. Towards this direction, this paper identifies a set of dimensions that distinguish, describe and categorize multi-criteria recommender systems, based on existing taxonomies and categorizations. These dimensions are integrated into an overall framework that is used for the analysis and classification of a sample of existing multi-criteria recommender systems. The results provide a comprehensive overview of the ways current multi-criteria recommender systems support the decision of online users.     

一个基于关联规则的个性化方法DPARM

针对当前个性化中关联规则挖掘的一些问题,尤其是无法及时更新使用数据这一缺点,提出了一种有效的基于关联规则挖掘的个性化方法DPARM,它将用户兴趣模型的更新和在线推荐紧密结合,及时使用新的用户会话更新用户兴趣模型,从而使个性化系统能够更好反映用户访问模式的变换。使用http://www.cs.depaul.edu上的数据进行了实验,结果表明,该方法是有效可行的。     

基于约束的旅游推荐系统的研究与设计

针对旅游领域的特点和传统推荐技术的应用局限性,提出一种基于约束的旅游推荐系统的设计方案,并对系统的推荐引擎进行了详细设计。系统通过可视化的知识获取工具高效地获取旅游领域知识、推荐规则、个性化规则等知识,使用交互＆个性化代理以会话式的交互方式逐步地启发用户的偏好和需要,利用多属性效用理论对推荐结果进行排序。相比传统的推荐方法,利用基于约束的推荐技术,能够为用户提供更加准确、个性化的旅游推荐服务。     

基于概念格和关联规则Web个人化系统

最近的一些研究提出将Web使用日志的挖掘技术应用于Web个人化系统中,用于克服传统个人化技术(如CF技术、基于内容的过滤技术)中存在的问题,如处理大数据量的能力较差,依赖于用户主观的登记信息,产生的用户描述是静态的,不能获取对象之间丰富的语义联系等.但是基于Web使用日志挖掘的个人化技术不能适用于用户的使用信息获取困难或者站点内容经常变化的情况.更有效的办法是将站点的内容特征和使用特征结合到一个Web挖掘结构中去,以备推荐引擎统一使用.提出了一个基于关联规则挖掘的个人化系统,它使用概念格作为存储频繁页面集的数据结构,并介绍了如何利用概念格实时地为当前活动用户产生推荐集.     

人工心理模型在个性化商品推荐系统中的应用

互联网的飞速发展和广泛应用,刺激了人们对推荐信息的需求。推荐系统的应运而生,减轻了信息过量对人们的威胁。目前,个性化已经成为一种发展趋势,而能使网站更具个性化的推荐系统也将逐渐成为一种必需的网上服务。本文提出了基于数量化I类理论的人工心理模型的建模方法,并介绍了该模型在个性化商品推荐系统中的应用。     

基于近邻关系的个性化推荐算法研究

协同过滤是目前电子商务推荐系统中广泛应用的最成功的推荐技术,但面临严峻的用户评分数据稀疏性和推荐实时性挑战。针对协同过滤中的数据稀疏问题,提出了一种基于最近邻的个性化推荐算法。通过维数简化技术对评分矩阵进行优化,降低数据稀疏性;采用一种新颖的相似性度量方法计算目标用户的最近邻居,产生推荐预测。实验结果表明,该算法有效地解决了数据稀疏,提高了推荐系统的推荐质量。     

基于图的混合推荐算法*

混合推荐是解决各种单一推荐方法缺陷的重要途径,文中提出基于图的混合推荐算法,通过在图中融合各种推荐因素进行建模,产生最终的推荐结果.利用推荐物品的内容属性计算物品间的相似度,构建最近邻图关联矩阵.根据物品的打分记录构建物品的兴趣模型,生成矢量函数.在此基础上,利用正则化框架组合关联矩阵和矢量函数,构建基于图的学习模型,实现基于图的混合推荐,并从理论上证明算法的收敛性.在MovieLens数据集和亚马逊网上商城交易数据上的对比实验验证文中算法的有效性.     

基于异质信息网络的推荐系统研究综述

推荐系统旨在为用户提供个性化匹配服务,从而有效缓解大数据时代的信息过载问题,并且改善用户体验,增加用户粘性,极大地促进了电子商务等领域的发展。然而,在实际应用场景中,由于数据稀疏和冷启动问题的存在,推荐系统往往难以得到精准的推荐结果;而复杂的模型设计也导致推荐系统的可解释性不尽如人意。因此,如何充分利用交互、属性、以及各种辅助信息提升推荐的性能和可解释性是推荐系统的核心问题。另一方面,异质信息网络作为一种全面地建模复杂系统中丰富的结构和语义信息的方法,在融合多源信息、捕捉结构语义等方面具有显著优势,已经被成功应用于相似性度量、节点聚类、链接预测、排序等各种数据挖掘任务中。近年来,采用异质信息网络统一建模推荐系统中不同类型对象的复杂交互行为、丰富的用户和商品属性以及各种各样的辅助信息,不仅有效地缓解了推荐系统的数据稀疏和冷启动问题,而且具有较好的可解释性,并因此得到了广泛关注与应用。本文旨在对基于异质信息网络的推荐系统进行全面地综述,首次系统地梳理现有工作,弥补该领域缺乏综述的空白。具体而言,本文首先介绍了异质信息网络和推荐系统的核心概念和背景知识,简要回顾了异质信息网络和推荐系统的研究现状,并且阐述了将推荐系统建模为异质信息网络的一般步骤。然后,本文根据模型原理的不同将现有方法分为三类,分别是基于相似性度量的方法、基于矩阵分解的方法和基于图表示学习的方法,并对每类方法的代表性工作进行了全面的介绍,指出了每类方法的优缺点和不同方法之间的发展脉络与内在关系。最后,本文讨论了现有方法存在的问题,并展望了该领域未来的几个潜在的研究方向。