(This workflow can be found on the KNIME Workflow Public Server under 050_Applications/050004_lastfm_Recommendations)
This KNIME workflow takes Social Media data from a popular music site and uses a predictive analytics technique to make music preference recommendations for the top artists. In addition, the workflow creates a multimedia report that shows the top artists and the other musicians associated with each in the form “Others who like X also like….”.
This workflow transforms the Social Media data to make it suitable for association, performs and advanced association analysis, utilizes the resulting statistics to select lists of artists and recommendations, combines this list with overall facts about the sample and enhances the artist data with pictures to create a dynamic multi-media report.
This dataset contains social networking, tagging, and music artist listening information from a set of 2K users from Last.fm online music system.
The dataset is released in the framework of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) at the 5th ACM Conference on Recommender Systems (RecSys 2011)