waashk/dblp huggingface dataset:
dynamic micro targeting: fitness-based approach to predicting individual preferences. it is crucial to segment customers intelligently in order to offer more targeted and personalized products and services. traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying clustering algorithms. recent research proposed a direct grouping-based approach that combines customers into segments by optimally combining transactional data of several customers and building a data mining model of customer behavior for each group. this paper proposes a new micro targeting method that builds predictive models of customer behavior not on the segments of customers but rather on the customer-product groups. this micro-targeting method is more general than the previously considered direct grouping method. we empirically show that it significantly outperforms the direct grouping and statistics-based segmentation methods across multiple experimental conditions and that it generates predominately small-sized segments, thus providing additional support for the micro-targeting approach to personalization. index terms: customer segmentation, marketing application, personalization, micro targeting, customer profiles
a text classification framework with a local feature ranking for learning social networks. in this paper, a text classifier framework with a feature ranking scheme is proposed to extract social structures from text data. it is assumed that only a small subset of relations between the individuals in a community is known. with this assumption, the social network extraction is translated into a classification problem. the relations between two individuals are represented by merging their document vectors and the given relations are used as labels of training data. by this transformation, a text classifier such as rocchio is used for learning the unknown relations. we show that there is a link between the intrinsic sparsity of social networks and class imbalance. furthermore, we show that feature ranking methods usually fail in problem with unbalanced data. in order to deal with this deficiency and re-balance the unbalanced social data, a local feature ranking method, which is called reverse discrimination, is proposed.
predicting blogging behavior using temporal and social networks. modeling the behavior of bloggers is an important problem with various applications in recommender systems, targeted advertising, and event detection. in this paper, we propose three models by combining content, temporal, social dimensions: the general blogging-behavior model, the profile-based blogging-behavior model and the socialnetwork and profile-based blogging-behavior model. the models are based on two regression techniques: extreme learning machine (elm), and modified general regression neural network (mgrnn). we choose one of the largest blogs, a political blog, dailykos 1, for our empirical evaluation. experiments show that the social network and profile-based blogging behavior model with elm regression techniques produce good results for the most active bloggers and can be used to predict blogging behavior.
web site recommendation using http traffic. collaborative filtering (cf) is widely used in web recommender systems, while most existing cf applications focus on transactions or page views within a single site. in this paper, we build a recommender system prototype, which suggests web sites to users, by collecting browsing events at routers without neither user nor website effort. 100 million http flows, involving 11, 327 websites, are converted to user-site ratings using access frequency as the implicit rating metric. with this rating dataset, we evaluate six cf algorithms including one proposed algorithm based on ip address locality. our experiments show that the recommendation from k nearest neighbors (rknn ) performs the best by 50% p@10 (precision of top 10) and 53% p@5 (precision of top 5). although the precision is far from ideal, our preliminary results suggest the potential value of such a centralized web site recommender system.
trend motif: a graph mining approach for analysis of dynamic complex networks. complex networks have been used successfully in scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. until recently, studies of complex networks have mainly focused on their network topology. however, in many real world applications, the edges and vertices have associated attributes that are frequently represented as vertex or edge weights. furthermore, these weights are often not static, instead changing with time and forming a time series. hence, to fully understand the dynamics of the complex network, we have to consider both network topology and related time series data. in this work, we propose a motif mining approach to identify trend motifs for such purposes. simply stated, a trend motif describes a recurring subgraph where each of its vertices or edges displays similar dynamics over a userdefined period. given this, each trend motif occurrence can help reveal significant events in a complex system; frequent trend motifs may aid in uncovering dynamic rules of change for the system, and the distribution of trend motifs may characterize the global dynamics of the system. here, we have developed efficient mining algorithms to extract trend motifs. our experimental validation using three disparate empirical datasets, ranging from the stock market, world trade, to a protein interaction network, has demonstrated the efficiency and effectiveness of our approach.
solving consensus and semi-supervised clustering problems using nonnegative matrix factorization. consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. consensus clustering (also known as aggregation of clustering) can improve clustering robustness, deal with distributed and heterogeneous data sources and make use of multiple clustering criteria. semi-supervised clustering can integrate various forms of background knowledge into clustering. in this paper, we show how consensus and semi-supervised clustering can be formulated within the framework of nonnegative matrix factorization (nmf). we show that this framework yields nmf-based algorithms that are: (1) extremely simple to implement; (2) provably correct and provably convergent. we conduct a wide range of comparative experiments that demonstrate the effectiveness of this nmf-based approach.
bandit-based algorithms for budgeted learning. we explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples' labels but has to pay for each attribute that is specified. this learning model is appropriate in many areas, including medical applications. we present new algorithms for choosing which attributes to purchase of which examples in the budgeted learning model based on algorithms for the multi-armed bandit problem. all of our approaches outperformed the current state of the art. furthermore, we present a new means for selecting an example to purchase after the attribute is selected, instead of selecting an example uniformly at random, which is typically done. our new example selection method improved performance of all the algorithms we tested, both ours and those in the literature.
multilevel belief propagation for fast inference on markov random fields. graph-based inference plays an important role in many mining and learning tasks. among all the solvers for this problem, belief propagation (bp) provides a general and efficient way to derive approximate solutions. however, for large scale graphs the computational cost of bp is still demanding. in this paper, we propose a multilevel algorithm to accelerate belief propagation on markov random fields (mrf). first, we coarsen the original graph to get a smaller one. then, bp is applied on the new graph to get a coarse result. finally the coarse solution is efficiently refined back to derive the original solution. unlike traditional multiresolution approaches, our method features adaptive coarsening and efficient refinement. the above process can be recursively applied to reduce the computational cost remarkably. we theoretically justify the feasibility of our method on gaussian mrfs, and empirically show that it is also effectual on discrete mrfs. the effectiveness of our method is verified in experiments on various inference tasks.
active learning from data streams. in this paper, we address a new research problem on active learning from data streams where data volumes grow continuously and labeling all data is considered expensive and impractical. the objective is to label a small portion of stream data from which a model is derived to predict newly arrived instances as accurate as possible. in order to tackle the challenges raised by data streams' dynamic nature, we propose a classifier ensembling based active learning framework which selectively labels instances from data streams to build an accurate classifier. a minimal variance principle is introduced to guide instance labeling from data streams. in addition, a weight updating rule is derived to ensure that our instance labeling process can adaptively adjust to dynamic drifting concepts in the data. experimental results on synthetic and real-world data demonstrate the performances of the proposed efforts in comparison with other simple approaches. *
depth-based novelty detection and its application to taxonomic research. it is estimated that less than 10 percent of the world's species have been described, yet species are being lost daily due to human destruction of natural habitats. the job of describing the earth's remaining species is exacerbated by the shrinking number of practicing taxonomists and the very slow pace of traditional taxonomic research. in this article, we tackle, from a novelty detection perspective, one of the most important and challenging research objectives in taxonomy new species identification. we propose a unique and efficient novelty detection framework based on statistical depth functions. statistical depth functions provide from the "deepest" point a "center-outward ordering" of multidimensional data. in this sense, they can detect observations that appear extreme relative to the rest of the observations, i.e., novelty. of the various statistical depths, the spatial depth is especially appealing because of its computational efficiency and mathematical tractability. we propose a novel statistical depth, the kernelized spatial depth (ksd) that generalizes the spatial depth via positive definite kernels. by choosing a proper kernel, the ksd can capture the local structure of a data set while the spatial depth fails. observations with depth values less than a threshold are declared as novel. the proposed algorithm is simple in structure: the threshold is the only one parameter for a given kernel. we give an upper bound on the false alarm probability of a depth-based detector, which can be used to determine the threshold. experimental study demonstrates its excellent potential in new species discovery.
parallel mining of frequent closed patterns: harnessing modern computer architectures. inspired by emerging multi-core computer architectures, in this paper we present mt closed, a multi-threaded algorithm for frequent closed itemset mining (fcim). to the best of our knowledge, this is the first fcim parallel algorithm proposed so far. we studied how different duplicate checking techniques, typical of fcim algorithms, may affect this parallelization. we showed that only one of them allows to decompose the global fcim problem into independent tasks that can be executed in any order, and thus in parallel. finally we show how mt closed efficiently harness modern cpus. we designed and tested several parallelization paradigms by investigating static/dynamic decomposition and scheduling of tasks, thus showing its scalability w.r.t. to the number of cpus. we analyzed the cache friendliness of the algorithm. finally, we provided additional speed-up by introducing simd extensions.
statistical learning algorithm for tree similarity. tree edit distance is one of the most frequently used distance measures for comparing trees. when using the tree edit distance, we need to determine the cost of each operation, but this is a labor-intensive and highly skilled task. this paper proposes an algorithm for learning the costs of tree edit operations from training data consisting of pairs of similar trees. to formalize the cost learning problem, we define a probabilistic model for tree alignment that is a variant of tree edit distance. then, the parameters of the model are estimated using the expectation maximization (em) technique. in this paper, we develop an algorithm for parameter learning that is polynomial in time (o(mn2d6)) and space (o(n2d4)) where n, d, and m represent the size of the trees, the maximum degree of trees, and the number of training pairs of trees, respectively.
efficient data sampling in heterogeneous peer-to-peer networks. performing data-mining tasks such as clustering, classification, and prediction on large datasets is an arduous task and, many times, it is an infeasible task given current hardware limitations. the distributed nature of peer-to-peer databases further complicates this issue by introducing an access overhead cost in addition to the cost of sending individual tuples over the network. we propose a two-level sampling approach focusing on peer-to-peer databases for maximizing sample quality given a user-defined communication budget. given that individual peers may have varying cardinality we propose an algorithm for determining the optimal sample rate (the percentage of tuples to sample from a peer) for each peer. we do this by analyzing the variance of individual peers, ultimately minimizing the total variance of the entire sample. by performing local optimization of individual peer sample rates we maximize approximation accuracy of the samples. we also offer several techniques for sampling in peer-to-peer databases given various amounts of known and unknown information about the network and its peers.
lightweight distributed trust propagation. using mobile devices, such as smart phones, people may create and distribute different types of digital content (e.g., photos, videos). one of the problems is that digital content, being easy to create and replicate, may likely swamp users rather than informing them. to avoid that, users may organize content producers that they know and trust in a web of trust. users may then reason about this web of trust to form opinions about content producers with whom they have never interacted before. these opinions will then determine whether content is accepted. the process of forming opinions is called trust propagation. we design a mechanism for mobile devices that effectively propagates trust and that is lightweight and distributed (as opposed to previous work that focuses on centralized propagation). this mechanism uses a graph-based learning technique. we evaluate the effectiveness (predictive accuracy) of this mechanism against a large real-world data set. we also evaluate the computational cost of a j2me implementation on a mobile phone.
social network extraction of academic researchers. this paper addresses the issue of extraction of an academic researcher social network. by researcher social network extraction, we are aimed at finding, extracting, and fusing the `semantic'-based profiling information of a researcher from the web. previously, social network extraction was often undertaken separately in an ad-hoc fashion. this paper first gives a formalization of the entire problem. specifically, it identifies the `relevant documents' from the web by a classifier. it then proposes a unified approach to perform the researcher profiling using conditional random fields (crf). it integrates publications from the existing bibliography datasets. in the integration, it proposes a constraints-based probabilistic model to name disambiguation. experimental results on an online system show that the unified approach to researcher profiling significantly outperforms the baseline methods of using rule learning or classification. experimental results also indicate that our method to name disambiguation performs better than the baseline method using unsupervised learning. the methods have been applied to expert finding. experiments show that the accuracy of expert finding can be significantly improved by using the proposed methods.
mechanism design for clustering aggregation by selfish systems. we propose a market mechanism that can be implemented on clustering aggregation problem among selfish systems, which tend to lie about their correct clustering during aggregation process. our study is the preliminary step toward the development of robust distributed data mining among selfish systems.
scalable collaborative filtering with jointly derived neighborhood interpolation weights. recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships. a predominant approach to collaborative filtering is neighborhood based (" k-nearest neighbors"), where a user-item preference rating is interpolated from ratings of similar items and/or users. we enhance the neighborhood-based approach leading to substantial improvement of prediction accuracy, without a meaningful increase in running time. first, we remove certain so-called "global effects" from the data to make the ratings more comparable, thereby improving interpolation accuracy. second, we show how to simultaneously derive interpolation weights for all nearest neighbors, unlike previous approaches where each weight is computed separately. by globally solving a suitable optimization problem, this simultaneous interpolation accounts for the many interactions between neighbors leading to improved accuracy. our method is very fast in practice, generating a prediction in about 0.2 milliseconds. importantly, it does not require training many parameters or a lengthy preprocessing, making it very practical for large scale applications. finally, we show how to apply these methods to the perceivably much slower user-oriented approach. to this end, we suggest a novel scheme for low dimensional embedding of the users. we evaluate these methods on the netflix dataset, where they deliver significantly better results than the commercial netflix cinematch recommender system.
exploration of link structure and community-based node roles in network analysis. communities are nodes in a network that are grouped together based on a common set of properties. while the communities and link structures are often thought to be in alignment, it may not be the case when the communities are defined using other external criterion. in this paper we provide a new way to measure the alignment. we also provide a new metric that can be used to estimate the number of communities to which a node is attached. this metric, along with degree, is used to assign a communitybased role to nodes. we demonstrate the usefulness of the community-based node roles by applying them to the influence maximization problem.
document transformation for multi-label feature selection in text categorization. feature selection on multi-label documents for automatic text categorization is an under-explored research area. this paper presents a systematic document transformation framework, whereby the multi-label documents are transformed into single-label documents before applying standard feature selection algorithms, to solve the multi-label feature selection problem. under this framework, we undertake a comparative study on four intuitive document transformation approaches and propose a novel approach called entropy-based label assignment (ela), which assigns the labels weights to a multi-label document based on label entropy. three standard feature selection algorithms are utilized for evaluating the document transformation approaches in order to verify its impact on multi-class text categorization problems. using a svm classifier and two multi-label evaluation benchmark text collections, we show that the choice of document transformation approaches can significantly influence the performance of multi-class categorization and that our proposed document transformation approach ela can achieve better performance than all other approaches.
a generalization of proximity functions for k-means. k-means is a widely used partitional clustering method. a large amount of effort has been made on finding better proximity (distance) functions for k-means. however, the common characteristics of proximity functions remain unknown. to this end, in this paper, we show that all proximity functions that fit k-means clustering can be generalized as k-means distance, which can be derived by a differentiable convex function. a general proof of sufficient and necessary conditions for k-means distance functions is also provided. in addition, we reveal that k-means has a general uniformization effect; that is, k-means tends to produce clusters with relatively balanced cluster sizes. this uniformization effect of k-means exists regardless of proximity functions. finally, we have conducted extensive experiments on various real-world data sets, and the results show the evidence of the uniformization effect. also, we observed that external clustering validation measures, such as entropy and variance of information (vi), have difficulty in measuring clustering quality if data have skewed distributions on class sizes.
recommendation via query centered random walk on k-partite graph. this paper presents an algorithm for recommending items using a diverse set of features. the items are recommended by performing a random walk on the k-partite graph constructed from the heterogenous features. to support personalized recommendation, the random walk must be initiated separately for each user, which is computationally demanding given the massive size of the graph. to overcome this problem, we apply multi-way clustering to group together the highly correlated nodes. a recommendation is then made by traversing the subgraph induced by clusters associated with a user's interest. our experimental results on real data sets demonstrate the efficacy of the proposed algorithm.
binary matrix factorization with applications. an interesting problem in nonnegative matrix factorization (nmf) is to factorize the matrix x which is of some specific class, for example, binary matrix. in this paper, we extend the standard nmf to binary matrix factorization (bmf for short): given a binary matrix x , we want to factorize x into two binary matrices w ,h (thus conserving the most important integer property of the objective matrix x ) satisfying x wh. two algorithms are studied and compared. these methods rely on a fundamental boundedness property of nmf which we propose and prove. this new property also provides a natural normalization scheme that eliminates the bias of factor matrices. experiments on both synthetic and real world datasets are conducted to show the competency and effectiveness of bmf.
weighted additive criterion for linear dimension reduction. linear discriminant analysis (lda) for dimension reduction has been applied to a wide variety of face recognition tasks. however, it has two major problems. first, it suffers from the small sample size problem when dimensionality is greater than the sample size. second, it creates subspaces that favor well separated classes over those that are not. in this paper, we propose a simple weighted criterion for linear dimension reduction that addresses the above two problems associated with lda. in addition, there are well established numerical procedures such as semi-definite programming for efficiently computing the proposed criterion. we demonstrate the efficacy of our proposal and compare it against other competing techniques using a number of examples.