In this paper we describe a novel approach to detecting power outages that utilizes social media platform users as “social sensors” for virtual detection of power outages. We present the underlying methodology based on analyzing Twitter and other social media data that detects bursts in...

The paper presents a method that uses aggregate ratingsprovided by various segments of users for various categoriesof items to derive better estimations of unknown individualratings. This is achieved by converting the aggregate ratingsinto constraints on the parameters of a rating...

This paper presents a novel probabilistic method for recommending items in the neighborhood-based collaborative filtering framework. For the probabilistic neighborhood selection phase, we use an efficient method for weighted sampling of k neighbors without replacement that also takes into...

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this paper, we propose a...

Most of the work on Context-Aware Recommender Systems (CARSes) has focused on demonstrating that the contextual information leads to more accurate recommendations and on developing efficient recommendation algorithms utilizing this additional contextual information. Little work has been done,...

Although the area of Context-Aware Recommender Systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem...