Combining Predictors for Recommending Music: the False Positives' approach to KDD Cup track 2

نویسندگان

  • Suhrid Balakrishnan
  • Rensheng Wang
  • Carlos Eduardo Scheidegger
  • Angus MacLellan
  • Yifan Hu
  • Aaron Archer
  • Shankar Krishnan
  • David Applegate
  • Guangqin Ma
  • S. Tom Au
چکیده

We describe our solution for the KDD Cup 2011 track 2 challenge. Our solution relies heavily on ensembling together diverse individual models for the prediction task, and achieved a final leaderboard misclassification rate of 3.8863%. This paper provides details on both the modeling and ensemble

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Based Prediction System for Recommendation : KDD Cup 2011 , Track 2

This paper describes a solution to the 2011 KDD Cup competition, Track2: discriminating between highly rated tracks and unrated tracks in a Yahoo! Music dataset. Our approach was to use supervised learning based on 65 features generated using various techniques such as collaborative filtering, SVD, and similarity scoring. During our modeling stage, we created a number of predictors including lo...

متن کامل

Taxonomy-Informed Latent Factor Models for Implicit Feedback

We describe an approach based on latent factor models to the Track 2 task of KDD Cup 2011, which required learning to discriminate between highly rated and unrated items from a large dataset of music ratings. We take the pairwise ranking route, training our models to rank the highly rated items above the unrated items which are sampled from the same distribution. Using the item relationship inf...

متن کامل

Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation

The track 2 problem in KDD Cup 2011 (music recommendation) is to discriminate between music tracks highly rated by a given user from those which are overall highly rated, but not rated by the given user. The training dataset consists of not only user rating history but also the taxonomic information of track, artist, album, and genre. This paper describes the solution of the National Taiwan Uni...

متن کامل

Feature Engineering in User's Music Preference Prediction

The second track of this year’s KDD Cup asked contestants to separate a user’s highly rated songs from unrated songs for a large set of Yahoo! Music listeners. We cast this task as a binary classification problem and addressed it utilizing gradient boosted decision trees. We created a set of highly predictive features, each with a clear explanation. These features were grouped into five categor...

متن کامل

Combining Factorization Model and Additive Forest for Collaborative Followee Recommendation

Social networks have become more and more popular in recent years. This popularity creates a need for personalization services to recommend tweets, posts (information) and celebrities organizations (information sources) to users according to their potential interest. Tencent Weibo (microblog) data in KDD Cup 2012 brings one such challenge to the researchers in the knowledge discovery and data m...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012