In event-based social networks, such as Meetup, social groups refer to self-organized communities that consist of users who share the same interests. In many real-world scenarios, users usually have social group preference and join interested social groups to attend events. It is therefore necessary to consider the influence of social groups to improve the event recommendation performance; however, existing event recommendation models generally consider users' individual preferences and neglect the influence of social groups. To this end, we propose a new Bayesian latent factor model SogBmf that combines social group influence and individual preference for event recommendation. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.

In this paper we study the friend recommendation problem in event-based social networks (EBSNs). Effective friend recommendation is of benefit to EBSNs, since it can promote user interaction and accelerate information diffusion for promoted events. Different from usual friend recommendations, the aim of making friends in EBSNs is to better participate offline events and enhance user experience. Meanwhile friend recommendation in EBSNs encounters three types of data, i.e. geographical information, implicate user rating, and user behavior. These differences imply that existing friend recommendation approaches are not adequate any more for EBSNs. Under this background, in this paper we propose a Bayesian latent factor model, which can jointly formulate above three types of data, for friend recommendation with better event promotion and user experience. Results on real-world datasets show the efficacy of our approach.

With the rapid growth of event-based social networks (EBSNs) like Meetup, the demand for event recommendation becomes increasingly urgent. In EBSNs, event recommendation plays a central role in recommending the most relevant events to users who are likely to participate in. Different from traditional recommendation problems, event recommendation encounters three new types of information, i.e., heterogenous online+offline social relationships, geographical features of events and implicit rating data from users. Yet combining the three types of data for offline event recommendation has not been considered. Therefore, we present a Bayesian latent factor model that can unify these data for event recommendation. Experimental results on real-world data sets show the performance of our method.

With the rapid growth of event-based social networks, the demand of event recommendation becomes increasingly important. Different from classic recommendation problems, event recommendation generally faces the problems of heterogenous online and offline social relationships among users and implicit feedback data. In this paper, we present a baysian probability model that can fully unleash the power of heterogenous social relations and efficiently tackle with implicit feedback characteristic for event recommendation. Experimental results on several real-world datasets demonstrate the utility of our method.

Max-margin matrix factorization (M3F) has been popularly applied to collaborative filtering for personalized recommendations. The nonparametric M3F model represents the latest progress of the M3F methods, which can auto-select the number of factors by using nonparametric techniques. However, existing non-parametric M3F methods assume a collection of user rating data can be fully obtained before training, and they are inapplicable for on-the-fly recommender systems where user rating data arrive continuously. In this paper, we present a new efficient online nonparametric 3F model for flexible recommendation. Specifically, we design an online nonparametric M3F model (OnM3F) based on the online Passive-Aggressive learning and solve the corresponding optimization problem by using the online stochastic gradient descent. Empirical studies on two large real-world data sets verify the effectiveness of the proposed method.

Detecting abnormalities from multiple correlated time series is valuable to those applications where a credible realtime event prediction system will minimize economic losses (e.g. stock market crash) and save lives (e.g. medical surveillance in the operating theatre). For example, in an intensive care scenario, anesthetists perform a vital role in monitoring the patient and adjusting the flow and type of anesthetics to the patient during an operation. An early awareness of possible complications is vital for an anesthetist to correctly react to a given situation. In this demonstration, we provide a comprehensive medical surveillance system to effectively detect abnormalities from multiple physiological data streams for assisting online intensive care management. Particularly, a novel online support vector regression (OSVR) algorithm is developed to approach the problem of discovering the abnormalities from multiple correlated time series for accuracy and real-time efficiency. We also utilize historical data streams to optimize the precision of the OSVR algorithm. Moreover, this system comprises a friendly user interface by integrating multiple physiological data streams and visualizing alarms of abnormalities.

Related data streams refer to data streams that can be joined together by matching their join attributes. Existing research on learning from related data streams is based on an assumption that all streams arrive at a central processing unit in a synchronous way, such that in an arbitrary sliding window, all tuples of the streams can be perfectly joined together. This assumption, however, does not hold when related data streams are generated or transferred at different speeds, and thus may arrive in the central processing unit in an asynchronous manner. In this paper, we argue that for asynchronous data streams, there exist a small portion of perfectly joined examples (i.e., complete examples) and a large portion of partially joined examples (i.e., incomplete examples). Accordingly, we present a new Learning from Complete and Fixed Examples (LCFE) framework that can fix incomplete examples to boost the learning. Experiments on both synthetic and real-world data streams demonstrate that LCFE is able to achieve a higher prediction accuracy for learning from related data streams than other simple solutions can offer.