The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.

In this paper, we target at four specific recommendation tasks in the academic environment: the recommendation for author coauthorships, paper citation recommendation for authors, paper citation recommendation for papers, and publishing venue recommendation for author-paper pairs. Different from previous work which tackles each of these tasks separately while neglecting their mutual effect and connection, we propose a joint multi-relational model that can exploit the latent correlation between relations and solve several tasks in a unified way. Moreover, for better ranking purpose, we extend the work maximizing MAP over one single tensor, and make it applicable to maximize MAP over multiple matrices and tensors. Experiments conducted over two real world data sets demonstrate the effectiveness of our model: 1) improved performance can be achieved with joint modeling over multiple relations; 2) our model can outperform three state-of-the art algorithms for several tasks.

We propose a novel probabilistic topic model that jointly models authors, documents, cited authors, and venues simultaneously in one integrated framework, as compared to previous work which embeds fewer components. This model is designed for three typical applications in academic network analysis: the problems of expert ranking, cited author prediction and venue prediction. Experiments based on two real world data sets demonstrate the model to be effective, and it outperforms several state-of-the-art algorithms in all three applications.

As early as the late nineteenth century, scientists began research in author attribution, mostly by identifying the writing styles of authors. Following research over centuries has repeatedly demonstrated that people tend to have distinguishable writing styles. Today we not only have more authors, but we also have all different kinds of publications: journals, conferences, workshops, etc., covering different topics and requiring different writing formats. In spite of successful research in author attribution, no work has been carried out to find out whether publication venues are similarly distinguishable by their writing styles. Our work takes the first step into exploring this problem. By approaching the problem using a traditional classification method, we extract three types of writing style-based features and carry out detailed experiments in examining the different impacts among features, and classification techniques, as well as the influence of venue content, topics and genres. Experiments on real data from ACM and Cite Seer digital libraries demonstrate our approach to be an effective method in distinguishing venues in terms of their writing styles.

One of the principal goals for most research scientists is to publish. There are many thousands of publications: journals, conferences, workshops, and more, covering different topics and requiring different writing formats. However, when a researcher that is new to a certain research domain finishes the work, it is sometimes difficult to find a proper place to submit the paper. To solve this problem, we provide a collaborative-filtering-based recommendation system that can provide venue recommendations to researchers. In particular, we consider both topic and writing-style information, and differentiate the contributions of different kinds of neighboring papers to make such recommendations. Experiments based on real-world data from ACM and Cite Seer digital libraries demonstrate that our approach can provide effective recommendations.

Each year many ACM SIG communities will recognize an outstanding researcher through an award in honor of his or her profound impact and numerous research contributions. This work is the first to investigate an automated mechanism to help in selecting future award winners. We approach the problem as a researchers' expertise ranking problem, and propose a temporal probabilistic ranking model which combines content with citation network analysis. Experimental results based on real-world citation data and historical awardees indicate that some kinds of SIG awards are well-modeled by this approach.

Web services have been emerging as a promising technology for business process integrations. Due to their long-duration and loosely coupled properties, Web service based applications require transactional support beyond traditional transactions. Some Web service standards have been proposed to deal with the transaction aspect of Web service applications. Compensation is a commonly used mechanism in these standards for backward recovery. However, the compensation mechanism usually adopted is too fixed and cannot satisfy the various requirements of different applications. In this paper, we first analyse the compensation protocol of current standards. Then we enrich the protocol by allowing flexible compensation and extend our proposed multiple-compensation mechanism in Web service environment. The implementation of the extended compensation mechanism is discussed and the incorporation of the mechanism into current standards is also addressed