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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)
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 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)
A vision-based vehicle type classification method using partial Gabor filter bank is present in this paper for five vehicles categorization: sedan, van, hatchback sedan, bus and van truck. To reduce the influence caused by the hues of vehicles, we extract the Gabor features from the edge image of vehicle, instead of from the grey image. Partial Gabor filter(More)
Event-based social networks (EBSNs), such as Meetup and Plancast, which offer platforms for users to plan, arrange, and publish events, have gained increasing popularity and rapid growth. EBSNs capture not only the online social relationship, but also the offline interactions from offline events. They contain rich heterogeneous information, including(More)
Heterogeneous networks refer to the networks comprising multiple types of entities as well as their interaction relationships. They arise in a great variety of domains, for example, event-based social networks Meetup and Plancast, and DBLP. Recommendation is a useful task in these heterogeneous network systems. Although many recommendation algorithms are(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)
The main aim of this paper is to develop a community discovery scheme in a multi-dimensional network for data mining applications. In online social media, networked data consists of multiple dimensions/entities such as users, tags, photos, comments, and stories. We are interested in finding a group of users who interact significantly on these media(More)