The Microsoft academic search dataset and KDD Cup 2013

Roy, Senjuti Basu; Cock, Martine De; Mandava, Vani; Savanna, Swapna; D'Alessandro, B; Perlich, Claudia; Cukierski, William; Hamner, Ben

doi:10.1145/2517288.2517299

Cited by 29 publications

(12 citation statements)

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“…We test its performance on Mean Average Precision (MAP) with different feature sets, different model parameters and different model adjusting strategies. The description of the dataset can be found in [9].…”

Section: Methodsmentioning

confidence: 99%

Contextual rule-based feature engineering for author-paper identification

Zhong

Wang

et al. 2013

Proceedings of the 2013 KDD Cup 2013 Workshop

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We present the ideas and methodologies that we used to address the KDD Cup 2013 challenge on author-paper identification. We firstly formulate the problem as a personalized ranking task and then propose to solve the task through a supervised learning framework. The key point is to eliminate those incorrectly assigned papers of a given author based on existing records. We choose Gradient Boosted Tree as our main classifier. Through our exploration we conclude that the most critical factor to achieve our results is the effective feature engineering. In this paper, we formulate this process as a unified framework that constructs features based on contextual information and combines machine learning techniques with human intelligence. Besides this, we suggest several strategies to parse authors' names, which improve the prediction results significantly. Divide-conquer based model building as well as the model averaging techniques also benefit the prediction precision.

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Section: Methodsmentioning

confidence: 99%

Contextual rule-based feature engineering for author-paper identification

Zhong

Wang

et al. 2013

Proceedings of the 2013 KDD Cup 2013 Workshop

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“…The task is to upload duplicated identifiers in Author.csv. Other details of data sets and the competition can be found in [7]. Because no training information is given, the problem is an unsupervised learning task.…”

Section: Introductionmentioning

confidence: 99%

Effective string processing and matching for author disambiguation

Chin

Juan

Zhuang

et al. 2013

Proceedings of the 2013 KDD Cup 2013 Workshop

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Track 2 in KDD Cup 2013 aims at determining duplicated authors in a data set from Microsoft Academic Search. This type of problems appears in many large-scale applications that compile information from different sources. This paper describes our solution developed at National Taiwan University to win the first prize of the competition. We propose an effective name matching framework and realize two implementations. An important strategy in our approach is to consider Chinese and non-Chinese names separately because of their different naming conventions. Post-processing including merging results of two predictions further boosts the performance. Our approach achieves F1-score 0.99202 on the private leader board, while 0.99195 on the public leader board.

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“…The goal of the Author-Paper Identification Challenge was to predict whether an author has written a paper assigned to them in the Microsoft Academic Search database (the detailed description of the dataset can be found in [9]). Thus, the task was a collaborative filtering task with a set of labeled training data and an unlabeled set of test data.…”

Section: Introductionmentioning

confidence: 99%