ST1 - Combining Multiple Metadata Types in Movies Recommendation Using Ensemble Algorithm

A session at XX Brazilian Symposium on Multimedia and the Web - Webmedia 2014

  • Bruno Souza Cabral
  • Frederico Durão
  • Renato Beltrão
  • Marcelo Garcia Manzato

Wednesday 19th November, 2014

2:00pm to 2:30pm (LMT)

In this paper, we analyze the application of ensemble algorithms to improve the ranking recommendation problem with multiple metadata. We proposed three generic ensemble strategies that do not require modification of the recommender algorithm. They combine predictions from a recommender trained with distinct metadata into a unified rank of recommended items. The proposed strategies are Most Pleasure, Best of All and Genetic Algorithm Weighting. The evaluation using the HetRec 2011 MovieLens 2k dataset with five different metadata (genres, tags, directors, actors and countries) shows that our proposed ensemble algorithms archive a considerable 7% improvement in the Mean Average Precision even with state-of-art collaborative filtering algorithms.

About the speakers

This person is speaking at this event.
Bruno Souza Cabral


This person is speaking at this event.
Frederico Durão


This person is speaking at this event.
Renato Beltrão


This person is speaking at this event.
Marcelo Garcia Manzato


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Time 2:00pm2:30pm LMT

Date Wed 19th November 2014

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