The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations. To solve these challenges, the objective item attributes are incorporated as complementary information. However, most of the existing methods for inferring the relationships between items assume that the attributes are "independently and identically distributed (iid)", which does not always hold in reality. In fact, the attributes are more or less coupled with each other by some implicit relationships. Therefore, in this pa-per we propose an attribute-based coupled similarity measure to capture the implicit relationships between items. We then integrate the implicit item coupling into MF to form the Coupled Item-based Matrix Factorization (CIMF) model. Experimental results on two open data sets demonstrate that CIMF outperforms the benchmark methods.
Content-based techniques are another successful method by which to recommend relevant items to users by matching the users' personal interests to descriptive item information @cite_20 @cite_23 @cite_22 . Generally, content-based methods are able to cope with the sparsity problem, however, they often assume an item's attributes are iid" which does not always hold in reality. Actually, several research outcomes @cite_7 @cite_19 @cite_13 @cite_4 have been proposed to handle these challenging issues. To the best of our knowledge, in relation to RS, there is only one paper @cite_21 which applies a coupled clustering method to group the items, then exploits CF to make recommendations. But from the perspective of RS, this paper does not fundamentally disclose the iid" assumption for items. This motivates us to analyze the intrinsic relationships from different levels to unfold the assumption.
@cite_4 @cite_22 @cite_7 @cite_21 @cite_19 @cite_23 @cite_13 @cite_20
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Clustering ensemble is a powerful approach for improving the accuracy and stability of individual (base) clustering algorithms. Most of the existing clustering ensemble methods obtain the final solutions by assuming that base clusterings perform independently with one another and all objects are independent too. However, in real-world data sources, objects are more or less associated in terms of certain coupling relationships. Base clusterings trained on the source data are complementary to one another since each of them may only capture some specific rather than full picture of the data. In this paper, we discuss the problem of explicating the dependency between base clusterings and between objects in clustering ensembles, and propose a framework for coupled clustering ensembles (CCE). CCE not only considers but also integrates the coupling relationships between base clusterings and between objects. Specifically, we involve both the intra-coupling within one base clustering (i.e., cluster label frequency distribution) and the inter-coupling between different base clusterings (i.e., cluster label co-occurrence dependency). Furthermore, we engage both the intra-coupling between two objects in terms of the base clustering aggregation and the inter-coupling among other objects in terms of neighborhood relationship. This is the first work which explicitly addresses the dependency between base clusterings and between objects, verified by the application of such couplings in three types of consensus functions: clustering-based, object-based and cluster-based. Substantial experiments on synthetic and UCI data sets demonstrate that the CCE framework can effectively capture the interactions embedded in base clusterings and objects with higher clustering accuracy and stability compared to several state-of-the-art techniques, which is also supported by statistical analysis. Coupled behaviors refer to the activities of one to many actors who are associated with each other in terms of certain relationships. With increasing network and community-based events and applications, such as group-based crime and social network interactions, behavior coupling contributes to the causes of eventual business problems. Effective approaches for analyzing coupled behaviors are not available, since existing methods mainly focus on individual behavior analysis. This paper discusses the problem of Coupled Behavior Analysis (CBA) and its challenges. A Coupled Hidden Markov Model (CHMM)-based approach is illustrated to model and detect abnormal group-based trading behaviors. The CHMM models cater for: 1) multiple behaviors from a group of people, 2) behavioral properties, 3) interactions among behaviors, customers, and behavioral properties, and 4) significant changes between coupled behaviors. We demonstrate and evaluate the models on order-book-level stock tick data from a major Asian exchange and demonstrate that the proposed CHMMs outperforms HMM-only for modeling a single sequence or combining multiple single sequences, without considering coupling relationships to detect anomalies. Finally, we discuss interaction relationships and modes between coupled behaviors, which are worthy of substantial study. Recommender systems are very useful due to the huge volume of information available on the Web. It helps users alleviate the information overload problem by recommending users with the personalized information, products or services (called items). Collaborative filtering and content-based recommendation algorithms have been widely deployed in e-commerce web sites. However, they both suffer from the scalability problem. In addition, there are few suitable similarity measures for the content-based recommendation methods to compute the similarity between items. In this paper, we propose a hybrid recommendation algorithm by combing the content-based and collaborative filtering techniques as well as incorporating the coupled similarity. Our method firstly partitions items into several item groups by using a coupled version of k-modes clustering algorithm, where the similarity between items is measured by the Coupled Object Similarity considering coupling between items. The collaborative filtering technique is then used to produce the recommendations for active users. Experimental results show that our proposed hybrid recommendation algorithm effectively solves the scalability issue of recommender systems and provides a comparable recommendation quality when lacking most of the item features. The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement. Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast,content-based methods use information about an item itself to make suggestions.This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations. The usual representation of quantitative data is to formalize it as an information table, which assumes the independence of attributes. In real-world data, attributes are more or less interacted and coupled via explicit or implicit relationships. Limited research has been conducted on analyzing such attribute interactions, which only describe a local picture of attribute couplings in an implicit way. This paper proposes a framework of the coupled attribute analysis to capture the global dependency of continuous attributes. Such global couplings integrate the intra-coupled interaction within an attribute (i.e. the correlations between attributes and their own powers) and inter-coupled interaction among different attributes (i.e. the correlations between attributes and the powers of others) to form a coupled representation for numerical objects by the Taylor-like expansion. This work makes one step forward towards explicitly addressing the global interactions of continuous attributes, verified by the applications in data structure analysis, data clustering, and data classification. Substantial experiments on 13 UCI data sets demonstrate that the coupled representation can effectively capture the global couplings of attributes and outperforms the traditional way, supported by statistical analysis. 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.