this repository is established to review codes and documents for our team ELP in SMPCUP2017, which is a user profiling contest based on massive data provided by CSDN, including text, relations and interactions of users.

In SMPCUP2017, every team is requested to work on three specific tasks:

• TASK1: Given a number of user documents (blogs or posts), generate 3 most appropriate keywords for each document. The generated keywords must appear in the document.
• TASK2: Given the user's document information (blog or post) and behavior data (browsing, comment, collection, forwarding, point-of-thumb, step-by-step, private, etc.) for each user, mark the three most relevant interests for each user. The label space is given by CSDN.
• TASK3: Given a number of users in a period of time (at least 1 year) document information (blog or post) and behavioral data (browsing, commentary, collection, forwarding, dating, attention, private messages, etc.), predict each user in the future over a period of time (half a year to 1 year) growth value. User growth is based on the user's overall performance scoring income, but will not publish the specific score criteria. The growth value will be normalized to the [0, 1] interval, where the value is 0 for user churn.

More detailed imformation could be browsed on the page: https://biendata.com/competition/smpcup2017/

• TASK1: TFIDF(Reference: https://en.wikipedia.org/wiki/Tf–idf#Term_frequency)
• TASK2: W-BAG, a model based on Word2Vec and BaggingClassifer
• TASK3: BPNN-XGboost(BPXG), a linear average model of BackPropagationNeuralNetwork and XGboost

• TASK1: S-TFIDF, a promoted model based on TFIDF and Textrank.
• TASK2: S-TFIDF/DocumentEmbedding-SVC-Stacking(SDSS), a stacking model that using S-TFIDF and DocumentEmbedding as first layer and using SVC as second layer.
• TASK3: PAR/GDR-NuSVR-Stacking(PGNS), a stacking model that using PassiveActiveRegressor and GrandientBoostingRegressor as first layer and using NuSVR as second layer.

• Task1: python 2.7
• Task2: python 3.0
• Task3: python 2.7

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