Userrank for item-based collaborative filtering recommendation

With the recent explosive growth of the Web, recommendation systems have been widely accepted by users. Item-based Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. A common problem of current item-based CF approaches is that all users have the same weight when computing the item relationships. To improve the quality of recommendations, we incorporate the weight of a user, userrank, into the computation of item similarities and differentials. In this paper, a data model for userrank calculations, a PageRank-based user ranking approach, and a userrank-based item similarities/differentials computing approach are proposed. Finally, the userrank-based approaches improve the recommendation results of the typical Adjusted Cosine and Slope One item-based CF approaches.     

协同过滤推荐系统中聚类搜索方法研究

最近邻计算是协同过滤方法中直接影响到推荐系统的运行效率和推荐准确率的重要一环。当用户和项目数目达到一定规模的时候,推荐系统的可扩展性明显降低。聚类的方法能在一定程度上弥补这个缺陷,但同时又会带来推荐准确性的下降。提出了一种与信息检索领域中的倒排索引相结合并采用“成员策略”的用户聚类搜索算法,缩短了最近邻计算的时间,实验的结果证明,该方法能在保证推荐正确性的前提下有效改善协同过滤推荐系统的可扩展性。     

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.     

基于用户购买记录的改进协同过滤推荐

提出一种基于用户购买记录的改进协同过滤推荐.用户的购买记录在一定程度上反映用户的偏好和购买习惯,采用用户项目购买数量矩阵,使用改进的相似度计算用户之间的相似度,结合用户购买商品之间的关联关系,对目标用户的购买数量进行预测,实现基于用户的协同过滤推荐.实验结果表明,该算法降低了数据稀疏性,使推荐结果更加精确可靠.     

一种基于特征表的协同过滤算法

协同过滤是迄今为止最成功的推荐系统,它可以产生高质量的推荐,但是其性能随着客户和产品数目的增加而下降.提出了一种基于特征表的协同过滤算法,该算法首先将原始数据划分成若干个特征集,然后通过建立特征表而避免顺序扫描.在真实数据集上的实验表明该算法对推荐系统的可伸缩性和推荐质量都有较大的提高.     

Applying artificial immune systems to collaborative filtering for movie recommendation

Collaborative filtering is a widely used recommendation technique and many collaborative filtering techniques have been developed, each with its own merits and drawbacks. In this study, we apply an artificial immune network to collaborative filtering for movie recommendation. We propose new formulas in calculating the affinity between an antigen and an antibody and the affinity of an antigen to an immune network. In addition, a modified similarity estimation formula based on the Pearson correlation coefficient is also developed. A series of experiments based on MovieLens and EachMovie datasets are conducted, and the results are very encouraging.     

A hybrid of sequential rules and collaborative filtering for product recommendation

Customers’ purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers’ purchase behavior may vary over time. In contrast, the sequential rule-based recommendation method analyzes customers’ purchase behavior over time to extract sequential rules in the form: purchase behavior in previous periods ⇒ purchase behavior in the current period. If a target customer’s purchase behavior history is similar to the conditional part of the rule, then his/her purchase behavior in the current period is deemed to be the consequent part of the rule. Although the sequential rule method considers the sequence of customers’ purchase behavior over time, it does not utilize the target customer’s purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method. The proposed method uses customers’ RFM (Recency, Frequency, and Monetary) values to cluster customers into groups with similar RFM values. For each group of customers, sequential rules are extracted from the purchase sequences of that group to make recommendations. Meanwhile, the segmentation-based KNN-CF method provides recommendations based on the target customer’s purchase data for the current period. Then, the results of the two methods are combined to make final recommendations. Experiment results show that the hybrid method outperforms traditional CF methods.     

Adaptive linguistic weighted aggregation operators for multi-criteria decision making

In this paper, we propose new aggregation operators for multi-criteria decision making under linguistic settings. The proposed operators are based on two sets of criteria weights. Besides the primary conventional criteria weights, we introduce a method to deduce secondary criteria weights from the criteria evaluations, which reflect the role of the different criteria in discriminating among the alternatives. The properties of the proposed operators are investigated. An approach for the application of the said operators in a group multi-criteria decision making problem is presented. Following the same, the proposed operators are applied in a case study on supplier selection. The empirical validation of the proposed operators is performed on a set of 12 real datasets.Note: All usages of he, him, his in the paper, also refer to she, and her.     

聚类与协同过滤相结合的隐式推荐系统

针对目前大多推荐系统中使用的协同过滤算法都需要有显示的用户反馈的问题,提出一种在隐式反馈推荐系统中使用聚类与矩阵分解技术相结合的方法,为用户提供更好地推荐结果。其结果是由基于用户历史购买记录的隐式反馈产生的,不需任何显式反馈提供的数据。采用高维的、无参数的分裂层次聚类技术产生聚类结果,根据聚类的结果为每个用户提供高兴趣度的个性化推荐。实验结果表明,在隐式反馈的情况下该方法也能有效获得用户偏好,产生大量的高准确度推荐。     

HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems

Recommender Systems (RS) have been being captured a great attraction of researchers by their applications in various interdisciplinary fields. Fuzzy Recommender Systems (FRS) is an extension of RS with the fuzzy similarity being calculated based on the users’ demographic data instead of the hard user-based degree. Based upon the observations that the FRS researches did not offer a mathematical definition of FRS accompanied with its algebraic operations and properties, and the fuzzy similarity degree is not enough to express accurately the analogousness between users, in this paper we will present a systematic mathematical definition of FRS including theoretical analyses of algebraic operations and properties and propose a novel hybrid user-based fuzzy collaborative filtering method that integrates the fuzzy similarity degrees between users based on the demographic data with the hard user-based degrees calculated from the rating histories into the final similarity degrees in order to obtain high accuracy of prediction. Experimental results on some benchmark datasets show that the proposed method obtains better accuracy than other relevant methods. Lastly, an application for the football results prediction is given to illustrate the uses of the proposed method.     

An integrated approach for structural damage identification using wavelet neuro-fuzzy model

Structural damage can be identified by processing structural vibration response signals and excitation data, and thus the suitability of signal processing methods is essential to structural damage identification. To explore an intelligent signal processing method for structural damage identification, the paper integrated wavelet real-time filtering algorithm, Adaptive Neruo-Fuzzy Inference System (ANFIS) and interval modeling technique to process structural response signals and excitation data. With Wavelet Transform (WT) algorithm filtering random noise, ANFIS was found to model the structural behavior properly and interval modeling technique to quantify damage index accurately. The rapid identifications of several unknown damages and small damages indicate the efficiency of this integrated method. The comparison of these results and some other signal processing methods shows that, the proposed method can be used to identify both the time and the location when the structural damage occurs unexpectedly.     

Textual case-based reasoning for spam filtering: a comparison of feature-based and feature-free approaches

Spam filtering is a text classification task to which Case-Based Reasoning (CBR) has been successfully applied. We describe the ECUE system, which classifies emails using a feature-based form of textual CBR. Then, we describe an alternative way to compute the distances between cases in a feature-free fashion, using a distance measure based on text compression. This distance measure has the advantages of having no set-up costs and being resilient to concept drift. We report an empirical comparison, which shows the feature-free approach to be more accurate than the feature-based system. These results are fairly robust over different compression algorithms in that we find that the accuracy when using a Lempel-Ziv compressor (GZip) is approximately the same as when using a statistical compressor (PPM). We note, however, that the feature-free systems take much longer to classify emails than the feature-based system. Improvements in the classification time of both kinds of systems can be obtained by applying case base editing algorithms, which aim to remove noisy and redundant cases from a case base while maintaining, or even improving, generalisation accuracy. We report empirical results using the Competence-Based Editing (CBE) technique. We show that CBE removes more cases when we use the distance measure based on text compression (without significant changes in generalisation accuracy) than it does when we use the feature-based approach.