Browsing by Author "DeLong, Colin"
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Item Concept-Aware Ranking: Teaching an Old Graph New Moves(2006-03-20) DeLong, Colin; Mane, Sandeep; Srivastava, JaideepRanking algorithms for web graphs, such as PageRank and HITS, are typically utilized for graphs where a node represents a unique URL (Webpage) and an edge is an explicitly-defined link between two such nodes. In addition to the link structure itself, additional information about the relationship between nodes may be available. For example, anchor text in a Web graph is likely to provide information about the underlying concepts connecting URLs. In this paper, we propose an extension to the Web graph model to take into account conceptual information encoded by links in order to construct a new graph which is sensitive to the conceptual links between nodes. By extracting keywords and recurring phrases from the anchor tag data, a set of concepts is defined. A new definition of a node (one which encodes both an URL and concept) is then leveraged to create an entirely new Web graph, with edges representing both explicit and implicit conceptual links between nodes. In doing so, inter-concept relationships can be modeled and utilized when using graph ranking algorithms. This improves result accuracy by not only retrieving links which are more authoritative given a users' context, but also by utilizing a larger pool of web pages that are limited by concept-space, rather than keyword-space. This is illustrated using webpages from the University of Minnesota's College of Liberal Arts websites.Item Social Topic Models for Community Extraction(2008-02-11) Pathak, Nishith; DeLong, Colin; Erickson, Kendrick; Banerjee, ArindamWith social interaction playing an increasingly important role in the online world, the capability to extract latent communities based on such interactions is becoming vital for a wide variety of applications. However, existing literature on community extraction has largely focused on methods based on the link structure of a given social network. Such link-based methods ignore the content of social interactions, which may be crucial for accurate and meaningful community extraction. In this paper, we present a Bayesian generative model for community extraction which naturally incorporates both the link and content information present in the social network. The model assumes that actors in a community communicate on topics of mutual interest, and the topics of communication, in turn, determine the communities. Further, the model naturally allows actors to belong to multiple communities. The model is instantiated in the context of an email network, and a Gibbs sampling algorithm is presented to do inference. Through extensive experiments and visualization on the Enron email corpus, we demonstrate that the model is able to extract well-connected and topically meaningful communities. Additionally, the model extracts relevant topics that can be mapped back to corresponding real-life events involving Enron.Item Team chemistry: the missing link in skill assessment for teams(2013-11) DeLong, ColinThe task of assessing the skill of players and teams in games is an old problem spanning numerous disciplinary fields and can be traced back to foundational work from the early 20th century. However, in the past 15 years, the arrival and immense popularity of online multi-player gaming has kindled new interest in skill assessment due to the importance of ensuring that automatically-generated competitions (in a process called "matchmaking") between perhaps millions of candidate players and teams are fair - that each player or team competing against one another has a roughly equal probability of winning a given game. Poor matchmaking has the effect of discouraging less-skilled players from continuing to play, which, in games that are increasingly reliant on multiplayer competition, is detrimental to a game's longevity and, therefore, its profitability. Beyond this problem, though, there exists a more general need to better account for attributes present in team-based games specifically, including the notion of "team chemistry" - a latent feature corresponding to the level of cohesion among teammates believed to impact the expected performance of teams not accounted for by the comparatively narrow lens of individual player skill alone. In this thesis, we introduce a skill assessment framework which accounts for the effects of "team chemistry" using the performances of subgroups of players in teams. These subgroups therefore form the atomic unit to which skill ratings are assigned and maintained, standing in stark contrast to the existing practice of assigning skill ratings to individual players only. Further, existing skill assessment algorithms, such as Elo, Glicko, or TrueSkill, can be easily modified to be utilized as "base learners" for the maintenance of these subgroup ratings. The final estimated overall skill of a team is then computed as an aggregation of these subgroup skill ratings, and we describe a number of novel approaches for doing so. Through experimentation, it is shown that several of these aggregation approaches greatly improve the likelihood of correctly predicting the outcomes of unseen games, and we draw a number of interesting conclusions based on evaluations conducted on datasets from online multi-player video games and real-world sports.