In a document network such as a citation network of scientific documents, web-logs etc., the content produced by authors exhibit their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Morover , we hypothesize that citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.

Fashion magazines contain a number of photographs of fashion models, and their clothing coordinates serve as useful references. In this paper, we propose a recommender system for clothing coordinates using full-body photographs from fashion magazines. The task is that, given a photograph of a fashion item (e.g. tops) as a query, to recommend a photograph of other fashion items (e.g. bottoms) that is appropriate to the query. With the proposed method, we use a probabilistic topic model for learning information about coordinates from visual features in each fashion item region. We demonstrate the effectiveness of the proposed method using real photographs from a fashion magazine and two fashion style sharing services with the task of making top (bottom) recommendations given bottom (top) photographs.

We have developed a novel hybrid representation for Music Information Retrieval. Our representation is built by incorporating audio content into the tag space in a tag-track matrix, and then learning hybrid concepts using latent semantic analysis. We apply this representation to the task of music recommendation, using similarity-based retrieval from a query music track. We also develop a new approach to evaluating music recommender systems, which is based upon the relationship of users liking tracks. We are interested in measuring the recommendation quality, and the rate at which cold-start tracks are recommended. Our hybrid representation is able to outperform a tag-only representation, in terms of both recommendation quality and the rate that cold-start tracks are included as recommendations.

Affinity Propagation is a state-of-the-art clustering method recently proposed by Frey and Dueck. It has been successfully applied to broad areas of computer science research because it has much better clustering performance than traditional clustering methods such as k -means. In order to obtain high quality sets of clusters, the original Affinity Propagation algorithm iteratively exchanges real-valued messages between all pairs of data points until convergence. However, this algorithm does not scale for large datasets because it requires quadratic CPU time in the number of data points to compute the messages. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters. Experimental evaluations on several different datasets demonstrate the effectiveness of our algorithm.

Extracting a subset of a given ontology that captures all the ontology's knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules. However, a single module does not allow us to understand neither topicality, connectedness, structure, or superfluous parts of an ontology, nor agreement between actual and intended modeling. The strong logical properties of locality-based modules suggest that the family of all such modules of an ontology can support comprehension of the ontology as a whole. However, extracting that family is not feasible, since the number of locality-based modules of an ontology can be exponential w.r.t. its size. In this paper we report on a new approach that enables us to efficiently extract a polynomial representation of the family of all locality-based modules of an ontology. We also describe the fundamental algorithm to pursue this task, and report on experiments carried out and results obtained.

Largely motivated by Semantic Web applications, many highly scalable, but incomplete, query answering systems have been recently developed. Evaluating the scalability-completeness trade-off exhibited by such systems is an important requirement for many applications. In this paper, we address the problem of formally comparing complete and incomplete systems given an ontology schema (or TBox) T. We formulate precise conditions on TBoxes T expressed in the EL, QL or RL profile of OWL 2 under which an incomplete system is indistinguishable from a complete one w.r.t. T, regardless of the input query and data. Our results also allow us to quantify the "degree of incompleteness" of a given system w.r.t. T as well as to automatically identify concrete queries and data patterns for which the incomplete system will miss answers.

Complex networks pervade in diverse areas ranging from the natural world to the engineered world and from traditional application domains to new and emerging domains, including web-based social networks. Of crucial importance to the understanding of many network phenomena, dynamics and functions is the study of network structural properties. One important type of network structure is known as community structure which refers to the existence of communities that are tightly knit local groups with relatively dense connections among their members. Community detection is the problem of detecting these communities automatically. In this paper, based on the modularity measure proposed previously for community detection, we first propose a reformulation of an optimization problem for the 2-partition problem. Based on this new formulation, we can extend it naturally for tackling the general k-partition problem directly without having to tackle multiple 2-partition subproblems like what other methods do. We then propose a convex relaxation scheme to give an iterative algorithm which solves a simple quadratic program in each iteration. We empirically compare our method with some related methods and find that our method is both scalable and competitive in performance via maintaining a good tradeoff between efficiency and quality.

Record linkage is the process of matching records between two (or multiple) data sets that represent the same real-world entity. An exhaustive record linkage process involves computing the similarities between all pairs of records, which can be very expensive for large data sets. Blocking techniques alleviate this problem by dividing the records into blocks and only comparing records within the same block. To be adaptive from domain to domain, one category of blocking technique formalizes 'construction of blocking scheme' as a machine learning problem. In the process of learning the best blocking scheme, previous learning-based techniques utilize only a set of labeled data. However, since the set of labeled data is usually not large enough to well characterize the unseen (unlabeled) data, the resultant blocking scheme may poorly perform on the unseen data by generating too many candidate matches. To address that, in this paper, we propose to utilize unlabeled data (in addition to labeled data) for learning blocking schemes. Our experimental results show that using unlabeled data in learning can remarkably reduce the number of candidate matches while keeping the same level of coverage for true matches.

We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach. The content-based part uses selected user profile features and similarity measure to generate a set of similar users. The collaborative filtering part uses the interactions of the similar users, including the people they like/dislike and are liked/disliked by, to produce reciprocal recommendations. CCR addresses the cold start problem of new users joining the site by being able to provide recommendations immediately, based on their profiles. Evaluation results show that the success rate of the recommendations is 69.26% compared with a baseline of 35.19% for the top 10 ranked recommendations.

In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of classes (label power-set methods, LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs do not scale well and BRMs ignore the dependency relations between classes. We introduce a method for chaining binary Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multidimensional classification. The method consists of two phases. In the first phase, a Bayesian network (BN) that represents the dependency relations between the class variables is learned from data. In the second phase, several chain classifiers are built, such that the order of the class variables in the chain is consistent with the class BN. At the end we combine the results of the different generated orders. Our method considers the dependencies between class variables and takes advantage of the conditional independence relations to build simplified models. We perform experiments with a chain of naive Bayes classifiers on different benchmark multidimensional datasets and show that our approach outperforms other state-of-the-art methods.