This paper was just published yesterday in International Conference on Recommender Systems 2012. Normally, traditional recommender systems mimic the “weighting” into sophisticated algorithms. All users get is a rank list of recommended items. This paper talks about the design of using interactive visualization to allow users to adjust the importance of the features, and items which are relevant for the recommendation. The idea is simple and allow users to understand and control aspects of the recommendation process.
TasteWeights is a hybrid recommender. It combines one’s music library and his friends (or other social media) music tastes together to generate recommendations.
Check the following system interface and a demo
This is an explanatory interface which educate users about recommendation systems and enable them to tweak the underlying algorithms in realtime. The study results show that the interface:
Do you find the similarities from what we did in today’s EdLab's Development & Research?
The zoomable researcher recommender interface presented by team Rebecca, Lu, Laura, Hui Soo. Totally spot it ☺
In the context of music, it may make sense to combine “social element” for the hybrid recommendations. In our context, we can consider more reciprocal recommendations mechanism for bettering matching.