Overview of kddcup 2011
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Overview of KDDCUP 2011. Nathan Liu [email protected] KDDCUP 2011 Music Recommendation. KDDCUP is the most prominent data mining competition. In recent years, there have been a number of contest related to movie recommendation: Netflix 2006: predict future ratings

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Overview of kddcup 2011

Overview of KDDCUP 2011

Nathan Liu

[email protected]


Kddcup 2011 music recommendation

KDDCUP 2011 Music Recommendation

  • KDDCUP is the most prominent data mining competition.

  • In recent years, there have been a number of contest related to movie recommendation:

    • Netflix 2006: predict future ratings

    • KDDCUP 2007: how many ratings and who rated what

    • CAMRA 2010: context aware movie recommendation

  • KDDCUP 2011 is organized by yahoo and provides the first and largest music ratings datasets.


Yahoo music

Yahoo Music


Kddcup 2011

KDDCUP 2011

  • There are three types of items: songs, artists, albums.

  • Songs and albums are annotated with genres.

  • You are given the date, time and scores of each user’s ratings of these different items.

  • Challenges:

    • Scale: biggest public dataset ever. 1 million user, 0.6 million items, 300 million ratings

    • Hierarchical item relation: song belong to albums, albums belong to artists. All of them are annotated with genre tags.

    • Rich meta data: over 900 genres

    • Fine temporal resolution: no previous challenge provided time in addition to date.

  • For the project, you will be provided with a small subset of the data and we will held a mini internal competition to determine which group obtained the best results.


Kddcup 2011 task 1

KDDCUP 2011: Task 1

  • The test set consists of hold out ratings from users in the training set. Each rating is time stamped.

  • In the test set, you are given who rated which items at what time.

  • You are asked to predict the rating scores.

  • Closely related to Netflix competition, but may require time of day effect consideration.

  • References:

    • Koren. Matrix Factorization Techniques for Recommender Systems. (IEEE Computer 2009)

    • Koren. Collaborative Filtering with Temporal Dynamics (KDD’09)

    • Xiong. Time-Evolving Collaborative Filtering (SDM’10)

    • Liu. Online Evolutionary Collaborative Filtering (RECSYS’10)


Kddcup 2011 task 2

KDDCUP 2011: Task 2

  • The test set consists of hold out ratings from users in the training set. Time has been removed.

  • In the test set, you are given 6 items for each user.

  • You are asked to predict which 3 of the 6 are actually rated by the user.

  • Closely related to KDDCUP 2007 “who rated what” and CAMRA2010 weekly recommendation track

  • References:

    • Hu. Collaborative Filtering for Implicit Feedback Datasets (ICDM’08)

    • Rendle. Bayesian Personalized Ranking from Implicit Feedback (UAI’09)

    • Cremonesi. Performance of Recommender Algorithms on Top-N Recommendation Tasks (RECSYS’10)

    • Steck. Training and Testing of Recommender Systems on Data Missing Not at Random (KDD’10)


For the project

For The Project

  • We will extract a subset for you to work on.

  • We will provide some basic algorithms.

  • You can choose to work on one of the two tasks.

  • The minimum requirement is that you should run thorough experiments with the provided algorithms and write a report on your findings about different algorithms.

  • There are also new things to try….


Things to try 1 ensemble

Things to Try (1): Ensemble

  • Same algorithm different parameter settings

  • Different algorithms

  • Stacking:

    • What meta learner? Gradient Boosted Decision Tree, Linear Regression

    • Any meta features? Tail vs. Head segmentation strategy

  • References:

    • Bao et. al. Stacking Recommendation Engines with Additional Meta-Features (RECSYS’09)

    • Jahrer et. al. Combining Predictions for Accurate Recommender Systems (KDD’10)


Things to try 3 exploiting item relations and genres

Things to Try (3): Exploiting Item Relations and Genres

  • From social network of users to networks of items.

  • Combining collaborative filtering with genre based prediction for alleviating sparseness.

  • References:

    • Ma. Recommender Systems with Social Regularization (WSDM’11)

    • Agarwal. Regression based Latent Factor Models (KDD’09)

    • Popescul. Probabilistic Models for Unified Collaborative and Content-based Recommendation in sparse-data environments (UAI’01)

    • Gunawardana. Tied Boltzman Machines for Cold Start Recommendations (RecSys’08)


Things to try 2 temporal dynamics

Things to Try (2): Temporal Dynamics

  • Various possible types of temporal dynamics:

    • Long term effect: people getting pickier over time

    • Short term effect: festival mood

    • Time of day effect: day time vs. night time preference

    • Periodicity: every Friday night is party time

  • References:

    • Koren. Collaborative Filtering with Temporal Dynamics (KDD’09)


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