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Trading in electronic markets is a topic of increasing interest within the artificial intelligence (AI) and electronic commerce research communities. As Internet marketplaces proliferate, programs that monitor and bid in these markets automatically—what we call “trading agents”—will play a significant role. Various AI research communities have held(More)
KDD Cup 2010 is an educational data mining competition. Participants are asked to learn a model from students’ past behavior and then predict their future performance. At National Taiwan University, we organized a course for KDD Cup 2010. Most student sub-teams expanded features by various binarization and discretization techniques. The resulting sparse(More)
An important problem in the area of homeland security is to identify suspicious entities in large datasets. Although there are methods from knowledge discovery and data mining (KDD) focusing on finding anomalies in numerical datasets, there has been little work aimed at discovering suspicious instances in large and complex semantic graphs whose nodes are(More)
Track 1 of KDDCup 2011 aims at predicting the rating behavior of users in the Yahoo! Music system. At National Taiwan University, we organize a course that teams up students to work on both tracks of KDDCup 2011. For track 1, we first tackle the problem by building variants of existing individual models, including Matrix Factorization, Restricted Boltzmann(More)
Label powerset (LP) method is one category of multi-label learning algorithm. This paper presents a basis expansions model for multi-label classification, where a basis function is a LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the ground truth. We derive an(More)
Part of the long lasting cultural heritage of China is the classical ancient Chinese poems which follow strict formats and complicated linguistic rules. Automatic Chinese poetry composition by programs is considered as a challenging problem in computational linguistics and requires high Artificial Intelligence assistance, and has not been well addressed. In(More)
A significant portion of knowledge discovery and data mining research focuses on finding patterns of interest in data. Once a pattern is found, it can be used to recognize satisfying instances. The new area of link discovery requires a complementary approach, since patterns of interest might not yet be known or might have too few examples to be learnable.(More)
Location-based services allow users to perform geospatial recording actions, which facilitates the mining of the moving activities of human beings. This article proposes to recommend time-sensitive trip routes consisting of a sequence of locations with associated timestamps based on knowledge extracted from large-scale timestamped location sequence data(More)
Location-based services allow users to perform geo-spatial check-in actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale check-in data. Given a query location(More)