Kunpeng Zhang

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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)
—Social Media is becoming major and popular technological platform that allows users discussing and sharing information. Information is generated and managed through either computer or mobile devices by one person and consumed by many other persons. Most of these user generated content are textual information, as Social Networks(Facebook, LinkedIn),(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 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)
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)
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)
Many machine learning, statistical, and computational linguistic methods have been developed to identify sentiment of sentences in documents, yielding promising results. However, most of state-of-the-art methods focus on individual sentences and ignore the impact of context on the meaning of a sentence. In this paper, we propose a method based on(More)
The exponential rise of online content in the form of blogs, microblogs, forums, and multimedia sharing sites has raised an urgent demand for efficient and high-quality text clustering algorithms for fast navigation and browsing of users based on better document organization. For several kinds of these user-generated content, it is much easier to obtain the(More)