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The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty(More)
The main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i)(More)
In this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search. The basic idea of our framework is to consider a random walk in multi-relational data, and study in such random walk, limiting probabilities of relations for relevance scores, and of(More)
For high dimensional data a large portion of features are often not informative of the class of the objects. Random forest algorithms tend to use a simple random sampling of features in building their decision trees and consequently select many subspaces that contain few, if any, informative features. In this paper we propose a stratified sampling method to(More)
With the rapid growth of location-based social networks, Point of Interest (POI) recommendation has become an important research problem. However, the scarcity of the check-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about POIs are available and(More)
In this paper we develop a new model and propose an iterative method to calculate stationary probability vector of a transition probability tensor arising from a higher-order Markov chain. Existence and uniqueness of such stationary probability vector are studied. We also discuss and compare the results of the new model with those by the eigenvector method(More)
Non-Gaussian statistic model, alpha stable distribution has gained much attention due to its generality to represent heavy-tailed and impulsive interference. Unfortunately, there is no closed form expression for the probability density function of alpha-stable distributions. Hereby, to develop the approximate expressions is of importance for signal(More)