Learn More
As online shopping becomes increasingly more popular, many shopping web sites encourage existing customers to add reviews of products purchased. These reviews make an impact on the purchasing decisions of potential customers. At Amazon.com for instance, some products receive hundreds of reviews. It is overwhelming and time restrictive for most customers to(More)
Increasingly large numbers of customers are choosing online shopping because of its convenience, reliability, and cost. As the number of products being sold online increases, it is becoming increasingly difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, customer reviews,(More)
The rapid evolution of modern social networks motivates the design of networks based on users' interests. Using popular social media such as Facebook and Twitter, we show that this new perspective can generate more meaningful information about the networks. In this paper, we model user-interest based networks by deducing intent from social media activities(More)
Most of the existing active learning algorithms assume all the category labels as independent or consider them in a "flat" structure. However, in reality, there are many applications in which the set of possible labels are often organized in a hierarchical structure. In this paper, we consider the problem of active learning when the categories are(More)
We investigate a class of emerging online marketing challenges in social networks; macro behavioral targeting (MBT) is introduced as non-personalized broadcasting efforts to massive populations. We propose a new probabilistic graph-ical model for MBT. Further, a linear-time approximation method is proposed to circumvent an intractable paramet-ric(More)
In complex dynamic systems, accurate forecasting of extreme events, such as hurricanes, is a highly underdetermined, yet very important sustainability problem. While physics-based models deserve their own merits, they often provide unreliable predictions for variables highly related to extreme events. In this paper, we propose a new supervised machine(More)
In the post-genome era, huge numbers of protein structures accumulate, but little is known about their function. It is time consuming and labour intensive to investigate them, e.g., enzyme catalytic properties, through in vivo or in vitro work. So in silico predictions could be a promising strategy to greatly shrink the list of potential targets. This work(More)
This paper proposes a method based on conditional random fields to incorporate sentence structure (syntax and semantics) and context information to identify sentiments of sentences within a document. It also proposes and evaluates two different active learning strategies for labeling sentiment data. The experiments with the proposed approach demonstrate a(More)
—Recommender systems are vital to the success of online retailers and content providers. One particular challenge in recommender systems is the " cold start " problem. The word " cold " refers to the items that are not yet rated by any user or the users who have not yet rated any items. We propose ELVER to recommend and optimize page-interest targeting on(More)