We study the problem of finding related forum posts to a post at hand. We developed a multi-segment matching technique that considers posts as a set of segments each one written with a different goal in its author mind and computes the relatedness between two posts based on the similarity of their respective segments that are intended for the same goal. The questions are how our method identifies such segments, how it figures out for what each segment is intended and how it exploits this information to rank the posts. We experimentally illustrate the effectiveness and efficiency of our segmentation method and overall approach of finding related forum posts.

We study the problem of finding related forum posts to a post at hand. In contrast to traditional approaches for finding related documents that perform content comparisons across the content of the posts as a whole, we consider each post as a set of segments, each written with a different goal in mind. We advocate that the relatedness between two posts should be based on the similarity of their respective segments that are intended for the same goal, i.e., are conveying the same intention. This means that it is possible for the same terms to weigh differently in the relatedness score depending on the intention of the segment in which they are found. We have developed a segmentation method that by monitoring a number of text features can identify the parts of a post where significant jumps occur indicating a point where a segmentation should take place. The generated segments of all the posts are clustered to form intention clusters and then similarities across the posts are calculated through similarities across segments with the same intention. We experimentally illustrate the effectiveness and efficiency of our segmentation method and our overall approach of finding related forum posts.

Human activity is almost always intentional, be it in a physical context or as part of an interaction with a computer system. By understanding why user-generated events are happening and what purposes they serve, a system can offer a significantly improved and more engaging experience. However, goals cannot be easily captured. Analyzing user actions such as clicks and purchases can reveal patterns and behaviors, but understanding the goals behind these actions is a different and challenging issue. Our work presents a unified, multidisciplinary viewpoint for goal management that covers many different cases where goals can be used and techniques with which they can be exploited. Our purpose is to provide a common reference point to the concepts and challenging tasks that need to be formally defined when someone wants to approach a data analysis problem from a goal-oriented point of view. This work also serves as a springboard to discuss several open challenges and opportunities for goal-oriented approaches in data management, analysis, and sharing systems and applications.

This tutorial provides a comprehensive and cohesive overview of goal modeling and recognition approaches by the Information Retrieval, the Artificial Intelligence and the Social Media communities. We will examine how these fields restrict the domain of study and how they capture notions easily perceived by humans' intuition but difficult to be formally defined and handled algorithmically. It is the purpose of this tutorial to provide a solid framework for placing existing work into perspective and highlight critical open challenges that will act as a springboard for researchers and practitioners in database systems, social data, and the Web, as well as developers of web-based, database-driven, and social applications, to work towards more user-centric systems and applications.