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Shanchan Wu


Publications by Shanchan Wu (bibliography)

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Wu, Shanchan, Gong, Leanna, Rand, William and Raschid, Louiqa (2012): Making recommendations in a microblog to improve the impact of a focal user. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 265-268. Available online

We present a microblog recommendation system that can help monitor users, track conversations, and potentially improve diffusion impact. Given a Twitter network of active users and their followers, and historical activity of tweets, retweets and mentions, we build upon a prediction tool to predict the Top K users who will retweet or mention a focal user, in the future [10]. We develop personalized recommendations for each focal user. We identify characteristics of focal users such as the size of the follower network, or the level of sentiment averaged over all tweets; both have an impact on the quality of personalized recommendations. We use (high) betweenness centrality as a proxy of attractive users to target when making recommendations. Our recommendations successfully identify a greater fraction of users with higher betweenness centrality, in comparison to the overall distribution of betweenness centrality of the ground truth users for some focal user.

© All rights reserved Wu et al. and/or ACM Press

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Wu, Shanchan, Rand, William and Raschid, Louiqa (2011): Recommendations in social media for brand monitoring. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 345-348. Available online

We present a recommendation system for social media that draws upon monitoring and prediction methods. We use historical posts on some focal topic or historical links to a focal blog channel to recommend a set of authors to follow. Such a system would be useful for brand managers interested in monitoring conversations about their products. Our recommendations are based on a prediction system that trains a ranking Support Vector Machine (RSVM) using multiple features including the content of a post, similarity between posts, links between posts and/or blog channels, and links to external websites. We solve two problems, Future Author Prediction (FAP) and Future Link Prediction (FLP), and apply the prediction outcome to make recommendations. Using an extensive experimental evaluation on a blog dataset, we demonstrate the quality and value of our recommendations.

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Wu, Shanchan and Wang, Wenyuan (2004): Result Comparison of Two Rough Set Based Discretization Algorithms. In: ICEIS 2004 2004. pp. 511-514.

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