HYBRID PROFILING FOR HYBRID MULTICRITERIA RECOMMENDATION BASED ON IMPLICIT MULTICRITERIA INFORMATION

Multicriteria recommender systems typically gather the user preferences by asking a user to rate different aspects of an item on a sliding scale explicitly. However, this approach could possibly cause intrusiveness and conflict on user preferences. For example, an individual's preference on each aspect of an item may conflict with an overall preference. To overcome such limitations, we proposed the hybrid profiling framework to generate a set of useful implicit dataset to support multicriteria recommender systems. We also proposed two hybrid multicriteria recommendation approaches, namely the user-attribute-based (UAB) and the user-item matching (UIM) to improve recommendation accuracy. Finally, we conducted experiments to confirm the efficiency of the proposed approaches. The experiments show that the profiling framework and two hybrid recommendation approaches can alleviate the problem in an intrusive manner and decrease the degree of preference conflict without decreasing the accuracy of the recommendation. They also show that our proposed hybrid multicriteria recommendation approaches can significantly outperform both the traditional collaborative filtering and the simple multicriteria filtering approaches.     

一种融合项目特征和移动用户信任关系的推荐算法

协同过滤推荐系统中普遍存在评分数据稀疏问题.传统的协同过滤推荐系统中的余弦、Pearson 等方法都是基于共同评分项目来计算用户间的相似度;而在稀疏的评分数据中,用户间共同评分的项目所占比重较小,不能准确地找到偏好相似的用户,从而影响协同过滤推荐的准确度.为了改变基于共同评分项目的用户相似度计算,使用推土机距离（earth mover's distance,简称EMD）实现跨项目的移动用户相似度计算,提出了一种融合项目特征和移动用户信任关系的协同过滤推荐算法.实验结果表明：与余弦、Pearson 方法相比,融合项目特征的用户相似度计算方法能够缓解评分数据稀疏对协同过滤算法的影响.所提出的推荐算法能够提高移动推荐的准确度.     

Context-aware recommendation using rough set model and collaborative filtering

Context has been identified as an important factor in recommender systems. Lots of researches have been done for context-aware recommendation. However, in current approaches, the weights of contextual information are the same, which limits the accuracy of the results. This paper aims to propose a context-aware recommender system by extracting, measuring and incorporating significant contextual information in recommendation. The approach is based on rough set theory and collaborative filtering. It involves a three-steps process. At first, significant attributes to represent contextual information are extracted and measured to identify recommended items based on rough set theory. Then the users’ similarity is measured in a target context consideration. Furthermore collaborative filtering is adopted to recommend appropriate items. The evaluation experiments show that the proposed approach is helpful to improve the recommendation quality.     

A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for products or services during a live interaction. These systems, especially collaborative filtering based on user, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the kinds of commodity to Web sites in recent years poses some key challenges for recommender systems. One of these challenges is ability of recommender systems to be adaptive to environment where users have many completely different interests or items have completely different content (We called it as Multiple interests and Multiple-content problem). Unfortunately, the traditional collaborative filtering systems can not make accurate recommendation for the two cases because the predicted item for active user is not consist with the common interests of his neighbor users. To address this issue we have explored a hybrid collaborative filtering method, collaborative filtering based on item and user techniques, by combining collaborative filtering based on item and collaborative filtering based on user together. Collaborative filtering based on item and user analyze the user-item matrix to identify similarity of target item to other items, generate similar items of target item, and determine neighbor users of active user for target item according to similarity of other users to active user based on similar items of target item.In this paper we firstly analyze limitation of collaborative filtering based on user and collaborative filtering based on item algorithms respectively and emphatically make explain why collaborative filtering based on user is not adaptive to Multiple-interests and Multiple-content recommendation. Based on analysis, we present collaborative filtering based on item and user for Multiple-interests and Multiple-content recommendation. Finally, we experimentally evaluate the results and compare them with collaborative filtering based on user and collaborative filtering based on item, respectively. The experiments suggest that collaborative filtering based on item and user provide better recommendation quality than collaborative filtering based on user and collaborative filtering based on item dramatically.     

Applications of wavelet data reduction in a recommender system

Memory-based collaborative filtering (CF) recommender systems have emerged as an effective technique for information filtering. CF recommenders are being widely adopted for e-commerce applications to assist users in finding and selecting items of interest. As a result, the scalability of CF recommenders presents a significant challenge; one that is particularly resilient because the volume of data these systems utilize will continue to increase over time. This paper examines the impact of discrete wavelet transformation (DWT) as an approach to enhance the scalability of memory-based collaborative filtering recommender systems. In particular, a wavelet transformation methodology is proposed and applied to both synthetic and real-world recommender ratings. For experimental purposes, the DWT methodology’s effect on predictive accuracy and calculation speed is evaluated to compare recommendation quality and performance.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

Sequence-based trust in collaborative filtering for document recommendation

Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users' past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users' various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users' sequences of ratings on documents. The model considers two factors – time factor and document similarity – in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems.     

一种检测兴趣漂移的图结构推荐系统

协同过滤是构造推荐系统最有效的方法之一.其中,基于图结构推荐方法成为近来协同过滤的研究热点.基于图结构的方法视用户和项为图的结点,并利用图理论去计算用户和项之间的相似度.尽管人们对图结构推荐系统开展了很多的研究和应用,然而这些研究都认为用户的兴趣是保持不变的,所以不能够根据用户兴趣的相关变化做出合理推荐.本文提出一种新的可以检测用户兴趣漂移的图结构推荐系统.首先,设计了一个新的兴趣漂移检测方法,它可以有效地检测出用户兴趣在何时发生了哪种变化.其次,根据用户的兴趣序列,对评分项进行加权并构造用户特征向量.最后,整合二部投影与随机游走进行项推荐.在标准数据集MovieLens上的测试表明算法优于两个图结构推荐方法和一个评分时间加权的协同过滤方法.     

基于Jaccard相似度和位置行为的协同过滤推荐算法

协同过滤是现今推荐系统中应用最为成功且最广泛的推荐方法之一,其中概率矩阵分解算法作为一类重要的协同过滤方式,能够通过学习低维的近似矩阵进行推荐。然而,传统的协同过滤推荐算法在推荐过程中只利用用户-项目评分信息,忽略了用户(项目)间的潜在影响力,影响了推荐精度。针对上述问题,首先利用Jaccard相似度对用户(项目)做预处理,而后通过用户(项目)间的位置信息挖掘出其间的潜在影响力,成功找到最近邻居集合;最后将该邻居集合融合到基于概率矩阵分解的协同过滤推荐算法中。实验证明该算法较传统的协同过滤推荐算法能够更有效地预测用户的实际评分,提高了推荐效果。     

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.     

An effective collaborative filtering algorithm based on user preference clustering

Collaborative filtering is one of widely used recommendation approaches to make recommendation services for users. The core of this approach is to improve capability for finding accurate and reliable neighbors of active users. However, collected data is extremely sparse in the user-item rating matrix, meanwhile many existing similarity measure methods using in collaborative filtering are not much effective, which result in the poor performance. In this paper, a novel effective collaborative filtering algorithm based on user preference clustering is proposed to reduce the impact of the data sparsity. First, user groups are introduced to distinguish users with different preferences. Then, considering the preference of the active user, we obtain the nearest neighbor set from corresponding user group/user groups. Besides, a new similarity measure method is proposed to preferably calculate the similarity between users, which considers user preference in the local and global perspectives, respectively. Finally, experimental results on two benchmark data sets show that the proposed algorithm is effective to improve the performance of recommender systems.     

基于二分图划分联合聚类的协同过滤推荐算法

准确而积极地向用户提供他们可能感兴趣的信息或服务是推荐系统的主要任务。协同过滤是采用得最广泛的推荐算法之一,而数据稀疏的问题往往严重影响推荐质量。为了解决这个问题,提出了基于二分图划分联合聚类的协同过滤推荐算法。首先将用户与项目构建成二分图进行联合聚类,从而映射到低维潜在特征空间;其次根据聚类结果改进2种相似性计算策略:簇偏好相似性和评分相似性,并将二者相结合。基于结合的相似性,分别采用基于用户和项目的方法来获得对未知目标评分的预测。最后,将这些预测结果进行融合。实验结果表明,所提算法比最新的联合聚类协同过滤推荐算法具有更好的性能。     

时间衰减制导的协同过滤相似性计算

协同过滤算法中,相似性计算方法是整个推荐系统的关键,决定着推荐系统的推荐质量,为了提高相似性计算的精准性,提出了一种基于时间衰减的相似性计算方法．该方法在计算用户相似性时,考虑目标物品与共同评分物品的相似性,同时在计算用户与物品相似性时,考虑时间信息（用户对物品产生行为的时间）对相似性的影响．实验结果表明,该方法能够有效地避免传统相似性计算方法的不足,使推荐系统获得更好的推荐效果．     

基于多序选择域的协同过滤推荐算法

传统的基于用户评分的协同过滤推荐系统无法找到合适的评分标准,对大量的评分数据挖掘不足,影响了用户的个性化表达。针对该问题,提出一种基于多序选择域的协同过滤推荐算法,采用选择域滑动匹配寻找项目关联性算法计算偏爱比较值,通过相似特征矩阵进行未评价项目的预测评价。实验结果表明,该推荐算法通过预测未评价项目可有效缓解数据的稀疏性,提高了推荐质量。     

对一个口令认证协议的可攻击性分析及改进

传统的基于用户评分的协同过滤推荐系统无法找到合适的评分标准,对大量的评分数据挖掘不足,影响了用户的个性化表达。针对该问题,提出一种基于多序选择域的协同过滤推荐算法,采用选择域滑动匹配寻找项目关联性算法计算偏爱比较值,通过相似特征矩阵进行未评价项目的预测评价。实验结果表明,该推荐算法通过预测未评价项目可有效缓解数据的稀疏性,提高了推荐质量。     

A hybrid approach for movie recommendation

Collaborative and content-based filtering are the major methods in recommender systems that predict new items that users would find interesting. Each method has advantages and shortcomings of its own and is best applied in specific situations. Hybrid approaches use elements of both methods to improve performance and overcome shortcomings. In this paper, we propose a hybrid approach based on content-based and collaborative filtering, implemented in MoRe, a movie recommendation system. We also provide empirical comparison of the hybrid approach to the base methods of collaborative and content-based filtering and draw useful conclusions upon their performance.     

一种结合评分重合度的协同推荐算法

协同推荐是信息个性化服务中广泛应用的推荐算法,协同推荐算法以宿主系统所观测到的用户评分作为实现推荐的数据依据。用户评分矩阵的稀疏性问题对协同推荐的各工作过程可产生直接或间接的影响,导致推荐服务的准确性下降。通过对稀疏性问题影响推荐系统方式的分析发现,一般协同推荐方法的项目相似度计算只注重项目在评分数值上的相关性,而忽视了项目之间评分的重合度对提高推荐质量所起的重要作用。通过将评分重合度融入到相似度计算中,提出了一种结合评分重合度的改进协同推荐算法,并在稀疏评分环境下将其与已有协同推荐算法进行了对比实验与分析,实验结果验证了所提算法在提高预测准确性上的有效性。     

耦合辅助信息的矩阵分解推荐模型

近十年来，协同过滤（CF）推荐系统成功地为用户提供了个性化的产品和服务。然而，用户—物品矩阵的稀疏性、推荐精度不高等问题仍然是一个挑战。针对这些问题，在矩阵分解模型基础上，提出了耦合用户和物品辅助信息的矩阵分解混合协同过滤框架；然后，基于此框架又提出了耦合物品属性信息相似度（COS）的过滤模型。大规模真实数据集上的实验表明，该模型不但可以有效解决物品相似度度量问题，而且相比传统方法，尤其是在物品特征非常稀疏的情况下，推荐准确性得到有效改进。     

Intelligent e‐government services with personalized recommendation techniques

Information overload is becoming one of the problems that hinder the effectiveness of e‐government services. Intelligent e‐government services with personalized recommendation techniques can provide a solution for this problem. Existing recommendation approaches have not entirely considered the influences of attributes of various online services and may result in no guarantee of recommendation accuracy. This study proposes a new approach to handle recommendation issues of one‐and‐only items in e‐government services. The proposed approach integrates the techniques of semantic similarity and the traditional item‐based collaborative filtering. A recommender system named Smart Trade Exhibition Finder has been developed to implement the proposed recommendation approach. The recommender system can be applied in e‐government services to improve the quality of government‐to‐business online services. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 401–417, 2007.     

基于概念层次树的个性化推荐算法

改进了传统的协同过滤算法,提出了基于概念层次树的用户模型,利用该模型进行协同运算,使系统在用户共同评分项极其稀疏时也能产生推荐。在相似性计算和产生推荐阶段引入了概念分层思想,分别在商品种类上产生推荐,避免了推荐的单一现象。MovieLens数据集实验表明,改进后的算法在推荐质量上有了明显的提高。